User identification method based on optical PUF authentication and related device

Abstract

The invention discloses a user identification method based on optical PUF authentication and a related device, and the method comprises the steps: determining a first consistency parameter between a first response speckle and a first standard speckle corresponding to a first excitation code, and carrying out the first user identification according to the first consistency parameter and a first identification threshold value; after the first identification is not passed, carrying out second user identification according to a plurality of second consistency parameters and a first identification threshold value; after the second identification is not passed, using a plurality of different second excitation codes to sequentially irradiate the PUF, so as to perform third user identification according to a plurality of third consistency parameters and a first identification threshold value; and after the identification is still not passed, threshold adjustment, feature point comparison, biological identification and other means are configured. According to the method, the same excitation code and different excitation codes are used in sequence to repeatedly irradiate and verify the user PUF, multiple other means are configured, system noise and interference of manual operation errors are eliminated from multiple angles, and the repeatability of user identification is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optical PUF authentication, and particularly to a user identification method based on optical PUF authentication, a user identification device based on optical PUF authentication, a user identification device based on optical PUF authentication, and a computer-readable storage medium. Background Art

[0002] User identification based on an optical physical unclonable function (PUF) has received extensive attention in the field of hardware security. However, in practical applications, due to problems such as PUF positioning device failures or user operation errors, legitimate users often face the situation of failed authentication. Therefore, to improve the repeatability of user identification and enhance system stability, complete reliance on hardware positioning devices cannot be achieved, and a specific speckle identification strategy needs to be adopted during user identification. However, traditional solutions usually rely on simple measurement methods to compare user speckles and cannot meet the requirements for high repeatability in practical applications. Therefore, how to provide a user identification method based on optical PUF authentication that can improve repeatability is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0003] The object of the present invention is to provide a user identification method based on optical PUF authentication, which has higher repeatability; another object of the present invention is to provide a user identification device based on optical PUF authentication, a user identification device based on optical PUF authentication, and a computer-readable storage medium, which have higher repeatability.

[0004] To solve the above technical problems, the present invention provides a user identification method based on optical PUF authentication, including:

[0005] Irradiating a user PUF with a first excitation code to obtain a first response speckle;

[0006] Determining a first consistency parameter between the first response speckle and a first standard speckle corresponding to the first excitation code, so as to perform a first user identification according to the first consistency parameter and a first identification threshold;

[0007] After the first user identification fails, re-irradiating the user PUF with the same first excitation code to continuously obtain a plurality of second response speckles;

[0008] Determining a second consistency parameter between each second response speckle and the first standard speckle, so as to perform a second user identification according to a plurality of second consistency parameters and the first identification threshold;

[0009] After the second user identification fails, irradiate the user's PUF with multiple different second activation codes in sequence, and after irradiating the user's PUF with each second activation code, obtain a third response speckle; each of the second activation codes corresponds to a second standard speckle;

[0010] Determine a third consistency parameter between each of the third response speckles and the second standard speckle corresponding to the second activation code, so as to perform a third user identification according to the multiple third consistency parameters and the first identification threshold;

[0011] After the third user identification fails, lower the first identification threshold to a second identification threshold;

[0012] Compare the third consistency parameter with the second identification threshold to perform a fourth user identification;

[0013] After the fourth user identification fails, determine the feature point set of any one of the first response speckle, the second response speckle, and the third response speckle and the feature point set of the corresponding standard speckle based on the feature point matching algorithm, and calculate the first similarity between the two groups of feature point sets;

[0014] Compare the first similarity with a third identification threshold to perform a fifth user identification;

[0015] After the fifth user identification fails, perform biometric identification on the user;

[0016] When the biometric identification passes, obtain activation information indicating that the user pulls out the PUF and reinserts it, and irradiate the user's PUF with a third activation code according to the activation information to obtain a fourth response speckle;

[0017] Based on the feature point matching algorithm, determine the feature point set of the fourth response speckle and the feature point set of the third standard speckle corresponding to the third activation code, and calculate the second similarity between the two groups of feature point sets;

[0018] Compare the second similarity with the third identification threshold to perform a sixth user identification.

[0019] Optionally, after determining the feature point set of the fourth response speckle and the feature point set of the third standard speckle corresponding to the third activation code based on the feature point matching algorithm, it further includes:

[0020] Determine the feature point set that mutually matches between the feature point set of the fourth response speckle and the feature point set of the third standard speckle;

[0021] Obtain the first coordinate value group of the matching feature point set in the fourth response speckle, and the second coordinate value group of the matching feature point set in the third standard speckle;

[0022] Determine the displacement mean value according to the first coordinate value group and the second coordinate value group;

[0023] Translate all pixel points in the fourth response speckle to the target coordinate points according to the displacement mean value to obtain a fifth response speckle; there is an overlapping area between the fifth response speckle and the fourth response speckle;

[0024] Authenticate based on the picture of the fifth response speckle located in the overlapping area and the picture of the third standard speckle located in the corresponding area to perform the seventh user identification on the user.

[0025] Optionally, calculating the first similarity between two sets of feature point sets includes:

[0026] Determine the number of feature points that match each other between the two sets of feature point sets;

[0027] Determine the percentage of the number of mutually matching feature points in the total number of feature points as the first similarity;

[0028] And / or, calculating the second similarity between two sets of feature point sets includes:

[0029] Determine the number of feature points that match each other between the two sets of feature point sets;

[0030] Determine the percentage of the number of mutually matching feature points in the total number of feature points as the second similarity.

[0031] Optionally, performing the second user identification according to multiple second consistency parameters and the first identification threshold includes:

[0032] When any one of the second consistency parameters exceeds the first identification threshold, determine that the second user identification is passed;

[0033] And / or, performing the third user identification according to multiple third consistency parameters and the first identification threshold includes:

[0034] When any one of the third consistency parameters exceeds the first identification threshold, determine that the third user identification is passed;

[0035] And / or, performing the fourth user identification includes:

[0036] When any one of the third consistency parameters exceeds the second identification threshold, determine that the fourth user identification is passed.

[0037] Optionally, determining the first consistency parameter between the first response speckle and the first standard speckle corresponding to the first excitation code includes:

[0038] Determine the Pearson correlation coefficient between the first response speckle and the first standard speckle corresponding to the first excitation code as the first consistency parameter;

[0039] And / or, determining the second consistency parameter between each of the second response speckles and the first standard speckle includes:

[0040] Determine the Pearson correlation coefficient between each of the second response speckles and the first standard speckle as the second consistency parameter;

[0041] And / or, determining the third consistency parameter between each of the third response speckles and the second standard speckle corresponding to the second excitation code includes:

[0042] Determine the Pearson correlation coefficient between each of the third response speckles and the second standard speckle corresponding to the second excitation code as the third consistency parameter.

[0043] Optionally, determining the first consistency parameter between the first response speckle and the first standard speckle corresponding to the first excitation code includes:

[0044] Based on the image filtering algorithm, determine the first binary texture feature image of the first response speckle and the second binary texture feature image of the first standard speckle;

[0045] Determine the first consistency parameter between the first binary texture feature image and the second binary texture feature image;

[0046] And / or, determining the second consistency parameter between each of the second response speckles and the first standard speckle includes:

[0047] Based on the image filtering algorithm, determine the third binary texture feature image of each of the second response speckles and the second binary texture feature image of the first standard speckle;

[0048] Determine the second consistency parameter between the second binary texture feature image and each of the third binary texture feature images;

[0049] And / or, determining the third consistency parameter between each of the third response speckles and the second standard speckle corresponding to the second excitation code includes:

[0050] Based on the image filtering algorithm, determine the fourth binary texture feature image of each of the third response speckles and the fifth binary texture feature image of each of the second standard speckles;

[0051] Determine the third consistency parameter between each of the fourth binary texture feature images and the corresponding fifth binary texture feature images.

[0052] Optionally, determining the first consistency parameter between the first binarized texture feature image and the second binarized texture feature image includes:

[0053] Determining the Hamming distance between the first binarized texture feature image and the second binarized texture feature image as the first consistency parameter;

[0054] Or, determining the Pearson correlation coefficient between the first binarized texture feature image and the second binarized texture feature image as the first consistency parameter;

[0055] Determining the second consistency parameter between the second binarized texture feature image and each of the third binarized texture feature images includes:

[0056] Determining the Hamming distance between the second binarized texture feature image and each of the third binarized texture feature images as the second consistency parameter;

[0057] Or, determining the Pearson correlation coefficient between the second binarized texture feature image and each of the third binarized texture feature images as the second consistency parameter;

[0058] Determining the third consistency parameter between each of the fourth binarized texture feature images and the corresponding fifth binarized texture feature image includes:

[0059] Determining the Hamming distance between each of the fourth binarized texture feature images and the corresponding fifth binarized texture feature image as the third consistency parameter;

[0060] Or, determining the Pearson correlation coefficient between each of the fourth binarized texture feature images and the corresponding fifth binarized texture feature image as the third consistency parameter. The present invention also provides a user identification device based on optical PUF authentication, including:

[0061] A first response speckle module, configured to irradiate a user PUF with a first excitation code to obtain a first response speckle;

[0062] A first user identification module, configured to determine a first correlation coefficient between the first response speckle and a first standard speckle corresponding to the first excitation code, so as to perform a first user identification according to the first correlation coefficient and a first identification threshold;

[0063] A second response speckle module, configured to, after the first user identification fails, irradiate the user PUF again with the same first excitation code to continuously obtain a plurality of second response speckles;

[0064] The second user identification module is used to determine the second correlation coefficient between each of the second response speckles and the first standard speckle, so as to perform a second user identification based on the multiple second correlation coefficients and the first identification threshold;

[0065] The third response speckle module is used to, after the second user identification fails, irradiate the user PUF with multiple different second excitation codes in sequence, and obtain a third response speckle after irradiating the user PUF with each second excitation code; each of the second excitation codes corresponds to a second standard speckle;

[0066] The third user identification module is used to determine the third correlation coefficient between each of the third response speckles and the second standard speckle corresponding to the second excitation code, so as to perform a third user identification based on the multiple third correlation coefficients and the first identification threshold;

[0067] The threshold reduction module is used to, after the third user identification fails, lower the first identification threshold to a second identification threshold;

[0068] The fourth user identification module is used to compare the third correlation coefficient with the second identification threshold to perform a fourth user identification;

[0069] The first similarity module is used to, after the fourth user identification fails, determine the feature points of any one of the first response speckle, the second response speckle, and the third response speckle and the feature points of the corresponding standard speckle based on the feature point matching algorithm, and calculate the first similarity between the two sets of feature points;

[0070] The fifth user identification module is used to compare the first similarity with a third identification threshold to perform a fifth user identification;

[0071] The biometric identification module is used to perform biometric identification on the user after the fifth user identification fails;

[0072] The fourth response speckle module is used to, after the biometric identification passes, obtain activation information indicating that the user pulls out the PUF and reinserts it, and irradiate the user PUF with a third excitation code according to the activation information to obtain a fourth response speckle;

[0073] The second similarity module is used to determine the feature points of the fourth response speckle and the feature points of the third standard speckle corresponding to the third excitation code based on the feature point matching algorithm, and calculate the second similarity between the two sets of feature points;

[0074] The sixth user identification module is used to compare the second similarity with the third identification threshold to perform a sixth user identification.

[0075] The present invention also provides a user identification device based on optical PUF authentication, and the device includes:

[0076] A memory: used for storing computer programs;

[0077] A processor: used for implementing the steps of the user identification method based on optical PUF authentication as described in any one of the above when executing the computer program.

[0078] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the user identification method based on optical PUF authentication as described in any one of the above are implemented.

[0079] For the user identification method based on optical PUF authentication provided by the present invention, after the first verification of the user PUF fails, verification will be carried out successively by repeatedly irradiating the user PUF with the same excitation code, changing the way of irradiating the user PUF with the excitation code for repeated verification, and after all verifications fail, by adjusting the recognition threshold, extracting the feature points of the speckle image for comparison, and combining biometric identification to let the user re-plug the PUF and then perform feature point comparison again, etc., so as to exclude the interference of system noise and human operation errors from multiple angles and improve the repeatability of user identification.

[0080] The present invention also provides a user identification device based on optical PUF authentication, a user identification device based on optical PUF authentication, and a computer-readable storage medium, which also have the above beneficial effects and will not be elaborated here. Description of the Drawings

[0081] In order to more clearly illustrate the technical solutions of the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0082] Figure 1 It is a flowchart of a user identification method based on optical PUF authentication provided by an embodiment of the present invention;

[0083] Figure 2 It is a flowchart of a specific user identification method based on optical PUF authentication provided by an embodiment of the present invention;

[0084] Figure 3 It is a flowchart of another specific user identification method based on optical PUF authentication provided by an embodiment of the present invention;

[0085] Figure 4 Flow chart of another specific user identification method based on optical PUF authentication provided by an embodiment of the present invention;

[0086] Figure 5 Schematic diagram for generating the fifth response speckle;

[0087] Figure 6 Structural block diagram of a user identification device based on optical PUF authentication provided by an embodiment of the present invention;

[0088] Figure 7 Structural block diagram of a user identification device based on optical PUF authentication provided by an embodiment of the present invention. Detailed implementation manners

[0089] The core of the present invention is to provide a user identification method based on optical PUF authentication. In the prior art, traditional solutions usually rely on simple measurement methods to compare user speckles, which cannot meet the requirements of high robustness in practical applications.

[0090] However, for a user identification method based on optical PUF authentication provided by the present invention, after the first verification of the user PUF fails, it will successively verify by repeatedly irradiating the user PUF with the same excitation code, changing the excitation code to irradiate the user PUF for verification, and adjusting the recognition threshold after all verifications fail, extracting the feature points of the speckle image for comparison, and combining biometric recognition to let the user re-plug the PUF and then perform feature point comparison again, etc., so as to exclude the interference of system noise and human operation errors from multiple angles and improve the repeatability of user identification.

[0091] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0092] Embodiment 1

[0093] Please refer to Figure 1 , Figure 1 Flow chart of a user identification method based on optical PUF authentication provided by an embodiment of the present invention.

[0094] See Figure 1 , in the embodiment of the present invention, the user identification method based on optical PUF authentication includes:

[0095] S101: Irradiate the user PUF with the first excitation code to obtain the first response speckle.

[0096] The above-mentioned user PUF is usually a device with a fixed entity, which may have a structure similar to a card or any other structure, and no specific limitation is made here. The above-mentioned challenge refers to a specific optical signal incident on the optical PUF. This optical signal is usually emitted by a light source and undergoes certain modulation or coding. Irradiating the encoded light onto the PUF key will generate a speckle response. Different challenges, that is, different encoded optical signals irradiating the PUF, will obtain different speckle responses.

[0097] In this step, first, irradiate the PUF (Physical Unclonable Function) device of the user with a preset first challenge. The first challenge can be a specific optical signal encoding, such as a laser signal with a certain wavelength, intensity, and modulation method. When this challenge irradiates the user PUF, due to the inhomogeneity of the internal microstructure of the PUF, a unique speckle image, that is, the first response speckle, will be generated. The process of obtaining this first response speckle in this step can be set according to the actual situation. For example, it can be collected through an image acquisition device such as a camera.

[0098] The above-mentioned first challenge can be a specific optical signal encoding randomly selected from a library, which can be generated by a laser modulator and has a preset wavelength, intensity, and spatial pattern. The random algorithm used in this random selection process can be determined according to the actual situation, and no specific limitation is made here.

[0099] The above-mentioned first challenge also corresponds to a first standard speckle. The first standard speckle is a speckle image obtained by irradiating the user PUF with the first challenge in an ideal state or preset conditions during the system initialization phase, and can be used as a reference benchmark in the subsequent authentication process. The acquisition method of the above-mentioned first response speckle can be to use a high-resolution industrial camera to align with the PUF device. When the challenge irradiates, the camera captures the speckle image and transmits it to the subsequent processing unit.

[0100] S102: Determine a first consistency parameter between the first response speckle and the first standard speckle corresponding to the first challenge, so as to perform the first user identification according to the first consistency parameter and the first recognition threshold.

[0101] After obtaining the first response speckle in this step, it is necessary to compare it with the pre-stored first standard speckle, where the first standard speckle corresponds to the first excitation code. The above-mentioned first consistency parameter can generally characterize the similarity between the first response speckle and the first standard speckle. For example, the Hamming distance or the Pearson correlation coefficient between the first response speckle and the first standard speckle can be used as the first consistency parameter. The specific type of the first consistency parameter can be set according to the actual situation and will not be specifically limited here. Specifically, this step may include: determining the Pearson correlation coefficient between the first response speckle and the first standard speckle corresponding to the first excitation code as the first consistency parameter. That is, this step can directly calculate the Pearson correlation coefficient between the first response speckle and the first standard speckle to determine the similarity between the two.

[0102] In this step, specifically, the first consistency parameter can be compared with the first recognition threshold, or user recognition can be performed in other ways, that is, the first user recognition. The specific content of the first recognition threshold can be set according to the actual situation and will not be specifically limited here. It is mainly used to determine whether the first consistency parameter meets the requirements and whether the current user PUF is the target user. The specific process of the above-mentioned first user recognition includes: when the first consistency parameter exceeds the first recognition threshold, it is determined that the first user recognition is passed.

[0103] It should be noted that in this embodiment, the specific value of the recognition threshold needs to be determined according to the type of the consistency parameter. For example, for the Hamming distance, the target threshold can be 0.3; for the Pearson correlation coefficient, the target threshold can be 0.7. Additionally, it should be stated that when comparing the consistency parameter with the recognition threshold in this application, the comparison criterion is not simply to look at the magnitude of the value. For example, for the Pearson correlation coefficient, the closer its value is to 1, the closer the two speckles are. Therefore, it is necessary to determine that the user recognition is passed when the Pearson correlation coefficient is greater than the target threshold, and vice versa, it is determined that the user recognition is not passed when the Pearson correlation coefficient is less than or equal to the target threshold; for the Hamming distance, the closer its value is to 0, the closer the two speckles are. Therefore, it is necessary to determine that the user recognition is passed when the Hamming distance is less than the target threshold, and vice versa, it is determined that the user recognition is not passed when the Hamming distance is greater than or equal to the target threshold.

[0104] S103: After the first user recognition fails, irradiate the user PUF again with the same first excitation code to continuously obtain multiple second response speckles.

[0105] If the first user identification fails, it indicates that the similarity between the currently obtained first response speckle and the first standard speckle is not high enough. The reason for this insufficient similarity may be the impact of accidental system noise on speckle identification. Therefore, in this step, the same excitation code will be used, that is, the first excitation code will be reused to irradiate the user PUF, and multiple response speckles will be continuously obtained. The response speckles obtained at this time are the second response speckles. The purpose of doing this is to reduce the impact of accidental factors on the identification result by obtaining the second response speckles multiple times, eliminate the impact of accidental system noise on speckle identification, and improve the accuracy of identification.

[0106] S104: Determine the second consistency parameters between each of the second response speckles and the first standard speckle, so as to perform a second user identification based on the multiple second consistency parameters and the first identification threshold.

[0107] This step is basically similar to the above S102. In this step, it is necessary to determine the consistency parameters, that is, the second consistency parameters, between each of the obtained second response speckles and the above first standard speckle. The specific data type of the second consistency parameter can be the same as or different from the above first consistency parameter. That is, the second consistency parameter can be the Hamming distance or the Pearson correlation coefficient between each of the second response speckles and the first standard speckle, and it can also be other parameters, which are not specifically limited here. Specifically, this step may include: determining the Pearson correlation coefficient between each of the second response speckles and the first standard speckle as the second consistency parameter. That is, this step can directly calculate the Pearson correlation coefficient between each of the second response speckles and the first standard speckle to determine the similarity between the two.

[0108] In this step, specifically, the second consistency parameters corresponding to each of the second response speckles can be compared with the first identification threshold respectively, or user identification can be performed through other calculation methods, that is, the second user identification. The specific content of the first identification threshold can be set according to the actual situation and is not specifically limited here. This second user identification process is mainly used to eliminate the impact of accidental system noise on speckle identification. The number of times of continuously obtaining the second response speckles in this step can be three or more, which is not specifically limited here.

[0109] The specific process of the above second user identification includes: when any of the second consistency parameters exceeds the first identification threshold, it is determined that the second user identification passes. When there is a second consistency parameter that exceeds the first identification threshold, it means that the user PUF can pass the authentication. Therefore, in this embodiment, as long as there is a second consistency parameter that exceeds the first identification threshold, it can be determined that the second user identification passes. On the contrary, when all the second consistency parameters do not exceed the first identification threshold, it will be determined that the second user identification fails.

[0110] S105: After the second user identification fails, irradiate the user PUF with multiple different second excitation codes in sequence, and after irradiating the user PUF with each second excitation code, obtain a third response speckle.

[0111] In the embodiment of the present invention, each of the second excitation codes corresponds to a second standard speckle. If the second user identification fails, it means that after excluding accidental system noise, the similarity between the response speckles captured based on the first excitation code and the first standard speckle is not high enough. The reason for this insufficient similarity may be that there are human operation errors or system noise affects the standard speckle during the registration phase. To exclude the influence during the registration phase, in this step, multiple different excitation codes will be used to irradiate the user PUF in sequence. The excitation codes used in this step are second excitation codes, and the number of excitation codes used in this step can be three or more, which is not specifically limited here. Correspondingly, three or more third response speckles can be obtained in this step.

[0112] In this step, multiple different excitation codes will be used to irradiate the PUF in sequence, and the corresponding response speckles, that is, third single response speckles, will be obtained after each irradiation. In this embodiment, each different second excitation code corresponds to a pre-stored standard speckle, that is, a second standard speckle. These standard speckles are usually the speckle images obtained by irradiating the PUF of this user with the corresponding excitation codes during the system initialization phase. By using multiple different second excitation codes to obtain multiple third response speckles, the characteristic information of the user PUF can be obtained from different angles and ways, excluding the influence of human operation errors or system noise on the standard speckle during the registration phase, and further improving the accuracy and reliability of the identification. For example, 3 different excitation codes can be used, and one third response speckle is obtained after each excitation code irradiation, for a total of 3 third response speckles.

[0113] S106: Determine the third consistency parameters between each of the third response speckles and the second standard speckles corresponding to the second excitation codes, so as to perform a third user identification based on the multiple third consistency parameters and the first identification threshold.

[0114] In each of the third response speckles obtained in this embodiment, there is a one-to-one correspondence with a second excitation code, and each second excitation code corresponds to a second standard speckle one by one. Therefore, in this embodiment, each third response speckle corresponds to a second standard speckle one by one. In this step, it is necessary to determine the consistency parameter, that is, the third consistency parameter, between each third response speckle and the second standard speckle corresponding thereto. The specific data type of the third consistency parameter may be the same as or different from the above-mentioned first consistency parameter. That is, the third consistency parameter may be the Hamming distance or the Pearson correlation coefficient between each third response speckle and the second standard speckle, or it may be other parameters, which are not specifically limited herein. Specifically, this step may include: determining the Pearson correlation coefficient between each of the third response speckles and the second standard speckle corresponding to the second excitation code as the third consistency parameter. That is, this step can directly calculate the Pearson correlation coefficient between each third response speckle and the corresponding second standard speckle to determine the similarity therebetween.

[0115] In this step, specifically, the third consistency parameter corresponding to each third response speckle can be compared with the first recognition threshold respectively, or user recognition can be performed through other arithmetic means, that is, the third user recognition. The specific content of the first recognition threshold can be set according to the actual situation and is not specifically limited herein. This third user recognition process is mainly used to exclude the influence of human operation errors or system noise during the registration stage on the standard speckle.

[0116] The specific process of the above-mentioned third user recognition includes: when any one of the third consistency parameters exceeds the first recognition threshold, it is determined that the third user recognition passes. When there is a third consistency parameter exceeding the first recognition threshold, it means that the user PUF can pass the authentication. Therefore, in this embodiment, as long as there is a third consistency parameter exceeding the first recognition threshold, it can be determined that the third user recognition passes. On the contrary, when all the third consistency parameters do not exceed the first recognition threshold, it is determined that the third user recognition fails.

[0117] S107: After the third user recognition fails, the first recognition threshold is lowered to the second recognition threshold.

[0118] The failure of the third user recognition means that after excluding accidental system noise and the influence of human operation errors or system noise during the registration stage on the standard speckle, the similarity between the corresponding speckle and the standard speckle is still not high enough. The reason for this insufficient similarity may be that the recognition threshold is set too high. To prevent legitimate users from being rejected by the system due to too high a threshold setting, in this step, it is necessary to lower the first recognition threshold to the second recognition threshold within a reasonable range. The specific value of this reasonable range can be set according to the actual situation and is not specifically limited herein.

[0119] It should be noted that the process of lowering the recognition threshold needs to be determined according to the specific type of the recognition threshold and the specific types of each consistency parameter. For example, if the recognition threshold corresponds to the Pearson correlation coefficient, the value of the first recognition threshold will be decreased during the lowering process. For example, it will be decreased from 0.7 to 0.6. If the recognition threshold corresponds to the Hamming distance, the value of the first recognition threshold will be increased during the lowering process. For example, it will be increased from 0.3 to 0.4 to lower the recognition standard.

[0120] S108: Compare the third consistency parameter with the second recognition threshold to perform the fourth user recognition.

[0121] Specifically in this step, the third consistency parameter is compared with the lowered second recognition threshold. The specific process of the fourth user recognition includes: when any one of the third consistency parameters is less than the second recognition threshold, it is determined that the fourth user recognition is passed. When there is a third consistency parameter exceeding the second recognition threshold, it means that the user PUF can pass the authentication. Therefore, in this embodiment, as long as there is a third consistency parameter exceeding the second recognition threshold, it can be determined that the fourth user recognition is passed. On the contrary, when all the third consistency parameters do not exceed the second recognition threshold, it is determined that the fourth user recognition fails.

[0122] S109: After the fourth user recognition fails, based on the feature point matching algorithm, determine the feature point set of any one of the first response speckle, the second response speckle, and the third response speckle and the feature point set of the corresponding standard speckle, and calculate the first similarity between the two groups of feature point sets.

[0123] The failure of the fourth user recognition means that the PUF authentication based on the consistency parameter cannot pass the user recognition. Therefore, in this step, it is necessary to calculate the feature points that match each other between the response speckle and the standard speckle based on the feature point matching algorithm to obtain the similarity based on the feature points, that is, the first similarity.

[0124] Feature point matching algorithms usually include at least three processes. The first process is to detect feature points in the image. The second process is to extract descriptors for each feature point. The third process is to perform matching between two sets of feature points based on the descriptors. When detecting feature points, usually a feature point detection algorithm is used to detect feature points in the first response speckle and the standard speckle respectively. The detected feature points are usually described as a feature vector or descriptor (descriptor). Feature points represent key information in the image, and they usually have invariance to rotation, scale, and illumination changes, enabling them to be accurately detected at different positions and angles in the image. Common feature point detection algorithms include the Harris corner detection algorithm, the FAST (Features from Accelerated Segment Test) algorithm, etc. An appropriate algorithm can be selected according to the actual application requirements and the characteristics of the speckle image (such as noise type, viewing angle change, etc.).

[0125] After feature point detection and descriptor extraction, usually a set of feature points of the selected response speckle and a corresponding set of feature points of the standard speckle are obtained. When performing feature point matching, the similarity between feature point descriptors is calculated to find similar feature point pairs in the two images. That is, similar feature points are found from the two sets of feature points to form feature point pairs. The two feature points constituting the feature point pair are the mutually matching feature points in the selected response speckle and the corresponding standard speckle.

[0126] In this step, the first similarity can be determined based on the number of mutually matching feature points in the two sets of feature points. That is, this step can specifically include: determining the number of mutually matching feature points between the two sets of feature points; determining the percentage of the number of mutually matching feature points in the total number of feature points as the first similarity. That is, in this step, the ratio of the number of mutually matching feature points in the total number of feature points can be used as a percentage. Among them, this step can calculate the percentage of the number of feature points corresponding to the standard speckle in the response speckle in the number of feature points in the response speckle, or the percentage of the number of feature points corresponding to the response speckle in the standard speckle in the number of feature points in the standard speckle, or the percentage of the total number of mutually matching feature points in the two images in the total number of feature points in the two images. When the number of feature points of the selected response speckle is equal to the number of feature points of the corresponding standard speckle, the above three percentage values are equal.

[0127] S110: Compare the first similarity with the third recognition threshold to perform the fifth user recognition.

[0128] In this step, specifically, the first similarity is compared with the third recognition threshold, which may be the same as or different from the above-mentioned second recognition threshold, depending on the specific situation. The specific process of the fifth user recognition includes: when the first similarity is less than the second recognition threshold, it is determined that the fourth user recognition is passed. When the first similarity is less than the third recognition threshold, it means that the user PUF can pass the authentication. On the contrary, when the first similarity is greater than or equal to the third recognition threshold, it is determined that the fifth user recognition fails.

[0129] S111: After the fifth user recognition fails, biometric identification of the user is performed.

[0130] The failure of the fifth user recognition means that the user needs to be identified by other means rather than just from the user PUF. In this step, specifically, biometric identification of the user is performed, that is, it is verified whether the biometric characteristics of the user conform to the pre-stored biometric characteristics. The biometric characteristics can specifically be fingerprint, face recognition, iris and other characteristic information, and no specific limitation is made here. In this step, the determination of the user's identity can be achieved by performing biometric identification of the user.

[0131] When the biometric identification of the user fails, it means that neither the user nor the PUF held by the user has been pre-registered, and at this time, the user's access will be refused. When the biometric identification of the user passes, it is determined that the user has been pre-registered but the PUF used did not pass the review in the previous step, and at this time, the subsequent steps will be executed.

[0132] S112: When the biometric identification passes, activation information indicating that the user pulls out the PUF and reinserts it is obtained, and the user PUF is irradiated with the third excitation code according to the activation information to obtain a fourth response speckle.

[0133] In this step, when the biometric identification passes, which means that the user has been pre-registered in advance, this embodiment allows the user to pull out the PUF and reinsert it into the system. Therefore, in this step, the activation information indicating that the user pulls out the PUF and reinserts it can be obtained. The specific content and specific form of the activation information can be set according to the actual situation, and no specific limitation is made here.

[0134] In this step, the user's PUF is irradiated with the third excitation code according to the activation information to obtain the fourth response speckle. The third excitation code needs to be at least different from the excitation code used to generate the response speckle selected in S109 above, and generally needs to be different from the first excitation code and the second excitation code above. In this step, the response speckle generated by irradiating the user's PUF with the third excitation code is the fourth response speckle. The standard speckle pre-stored corresponding to the third excitation code is the third standard speckle, which is generally the speckle image obtained by irradiating the user's PUF with the third excitation code during the system initialization phase.

[0135] S113: Determine the feature point set of the fourth response speckle and the feature point set of the third standard speckle corresponding to the third excitation code based on the feature point matching algorithm, and calculate the second similarity between the two sets of feature points.

[0136] This step is similar to S109 above. The feature points of the fourth response speckle and the third standard speckle will be determined based on the feature point matching algorithm. For the specific content of the feature matching algorithm, please refer to the previous content and will not be elaborated here. Correspondingly, the calculation of the second similarity is also the same as the calculation process of the previous first similarity. In this step, the number of feature points that match each other in the feature points of the fourth response speckle and the feature points of the third standard speckle will be determined, and then the percentage of the number of mutually matching feature points in the total number of feature points will be determined as the second similarity.

[0137] S114: Compare the second similarity with the third recognition threshold to perform the sixth user recognition.

[0138] In this step, specifically, the second similarity is compared with the third recognition threshold. The specific process of the sixth user recognition includes: when the second similarity is less than the second recognition threshold, it is determined that the sixth user recognition passes. When the second similarity is less than the third recognition threshold, it means that the user's PUF can pass the authentication. On the contrary, when the second similarity is greater than or equal to the third recognition threshold, it is determined that the sixth user recognition fails.

[0139] A user recognition method based on optical PUF authentication provided by an embodiment of the present invention, after the first verification of the user's PUF fails, it will successively verify by repeatedly irradiating the user's PUF with the same excitation code, change the way of irradiating the user's PUF with the excitation code to perform repeated verification, and after all verifications fail, adjust the recognition threshold, extract the feature points of the speckle image for comparison, and combine biometric recognition to let the user re-plug the PUF and then perform feature point comparison again, etc., so as to exclude the interference of system noise and human operation errors from multiple angles and improve the repeatability of user recognition.

[0140] The specific content of a user identification method based on optical PUF authentication provided by the present invention will be introduced in detail in the following invention embodiments and will not be elaborated here.

[0141] Embodiment 2

[0142] Please refer to Figure 2 , Figure 2 which is a flowchart of a specific user identification method based on optical PUF authentication provided by the embodiment of the present invention.

[0143] Refer to Figure 2 , in the embodiment of the present invention, the user identification method based on optical PUF authentication includes:

[0144] S201: Irradiate the user PUF with the first excitation code to obtain the first response speckle.

[0145] This step is basically the same as S101 in the above embodiment. For the detailed content, please refer to the above embodiment and will not be elaborated here.

[0146] S202: Based on the image filtering algorithm, determine the first binary texture feature picture of the first response speckle and the second binary texture feature picture of the first standard speckle.

[0147] In this step, image filtering algorithms such as the Gabor filtering algorithm can be used to process the above two speckle images. The specific content of image filtering algorithms such as the Gabor filtering algorithm can be referred to the prior art and will not be elaborated here. In this step, specifically, the Gabor filtering algorithm can be used to process the first response speckle and the first standard speckle respectively, so as to obtain the first binary texture feature picture corresponding to the first response speckle and the second binary texture feature picture corresponding to the first standard speckle.

[0148] S203: Determine the first consistency parameter between the first binary texture feature picture and the second binary texture feature picture, so as to perform the first user identification according to the first consistency parameter and the first recognition threshold.

[0149] The consistency parameter is used to characterize the degree of consistency or similarity of the above two binary texture feature pictures. The first parameter can be the Pearson correlation coefficient or the Hamming distance, etc. The specific calculation process of the Hamming distance or the Pearson correlation coefficient can be referred to the prior art and will not be elaborated here. The consistency parameter can characterize the degree of difference between the first binary texture feature picture and the second binary texture feature picture.

[0150] Taking the Hamming distance as an example, the smaller the Hamming distance, the higher the similarity between two images. In this step, when the Hamming distance is less than the first recognition threshold, it means that there is a high similarity between the first response speckle and the first standard speckle, and correspondingly, it can be determined that the user authentication passes at this time. On the contrary, when the Hamming distance is greater than or equal to the first recognition threshold, it means that there is a large difference between the first response speckle and the first standard speckle, and correspondingly, it can be determined that the user authentication fails at this time. At this time, this step may specifically include: determining the Hamming distance between the first binarized texture feature image and the second binarized texture feature image as the first consistency parameter.

[0151] Taking the Pearson correlation coefficient as an example, the larger the Pearson correlation coefficient, the higher the similarity between two images. In this step, when the Pearson correlation coefficient is greater than the first recognition threshold, it means that there is a high similarity between the first response speckle and the first standard speckle, and correspondingly, it can be determined that the user authentication passes at this time. On the contrary, when the Pearson correlation coefficient is less than or equal to the first recognition threshold, it means that there is a large difference between the first response speckle and the first standard speckle, and correspondingly, it can be determined that the user authentication fails at this time. At this time, this step may specifically include: determining the Pearson correlation coefficient between the first binarized texture feature image and the second binarized texture feature image as the first consistency parameter.

[0152] The above first recognition threshold needs to correspond to the type of the first consistency parameter. For example, corresponding to the Hamming distance, the first recognition threshold can be 0.3; corresponding to the Pearson correlation coefficient, the first recognition threshold can be 0.7, which is not specifically limited here.

[0153] S204: After the first user recognition fails, the user PUF is irradiated again with the same first excitation code, and multiple second response speckles are continuously obtained.

[0154] This step is basically the same as S103 in the above embodiment. For detailed content, please refer to the above embodiment and will not be elaborated here.

[0155] S205: Based on the image filtering algorithm, determine the third binarized texture feature images of each second response speckle and the second binarized texture feature image of the first standard speckle.

[0156] In this step, image filtering algorithms such as the Gabor filtering algorithm can be used to process each second response speckle and the first standard speckle respectively, so as to obtain multiple third binarized texture feature images corresponding one by one to the second response speckles, and the second binarized texture feature image corresponding to the first standard speckle.

[0157] S206: Determine the second consistency parameters between the second binarized texture feature image and each third binarized texture feature image, and perform a second user identification based on multiple second consistency parameters and a first recognition threshold.

[0158] In this step, the consistency parameter between each third binarized texture feature image and the second binarized texture feature image can be determined respectively, denoted as the second consistency parameter. Multiple second consistency parameters can be obtained in this step. The second consistency parameter can characterize the difference degree between the third binarized texture feature image and the second binarized texture feature image. In this step, the Hamming distance or Pearson correlation coefficient between the above-mentioned speckles can be calculated as the second consistency parameter. That is, this step can specifically include determining the Hamming distance between the second binarized texture feature image and each of the third binarized texture feature images as the second consistency parameter; or this step can include: determining the Pearson correlation coefficient between the second binarized texture feature image and each of the third binarized texture feature images as the second consistency parameter.

[0159] In this step, when any one of the second consistency parameters exceeds the first recognition threshold, it means that there is a high similarity between the second response speckles and the first standard speckles. Accordingly, it can be determined that the user authentication passes at this time. On the contrary, when each second consistency parameter does not exceed the first recognition threshold, it means that there are large differences between each second response speckle and the first standard speckle. Accordingly, it can be determined that the user authentication fails at this time.

[0160] S207: After the second user identification fails, irradiate the user PUF with multiple different second excitation codes in sequence, and obtain third response speckles after irradiating the user PUF with each second excitation code.

[0161] This step is basically the same as S105 in the above embodiment. For detailed content, please refer to the above embodiment and will not be elaborated here.

[0162] S208: Based on an image filtering algorithm, determine the fourth binarized texture feature images of each third response speckle and the fifth binarized texture feature images of each second standard speckle.

[0163] In this step, image filtering algorithms such as the Gabor filtering algorithm can be used to process each third response speckle and each second standard speckle respectively, so as to obtain multiple fourth binarized texture feature images corresponding one by one to the third response speckles, and obtain multiple fifth binarized texture feature images corresponding one by one to the second standard speckles. Since the third response speckles and the second standard speckles are in a one-to-one correspondence, the above-mentioned fourth binarized texture feature images and the fifth binarized texture feature images also have a one-to-one correspondence.

[0164] S209: Determine the third consistency parameter between each fourth binary texture feature image and the corresponding fifth binary texture feature image, so as to perform a third user identification based on multiple third consistency parameters and a first recognition threshold.

[0165] In this step, the third consistency parameter between each fourth binary texture feature image and the corresponding fifth binary texture feature image can be determined respectively, and multiple third consistency parameters can be obtained in this step. The third consistency parameter can characterize the difference degree between the fourth binary texture feature image and the fifth binary texture feature image. In this step, the Hamming distance or the Pearson correlation coefficient between the above-mentioned speckles can be calculated as the third consistency parameter. That is, this step can specifically include determining the Hamming distance between each of the fourth binary texture feature images and the corresponding fifth binary texture feature images as the third consistency parameter; or this step can include: determining the Pearson correlation coefficient between each of the fourth binary texture feature images and the corresponding fifth binary texture feature images as the third consistency parameter.

[0166] In this step, when any one of the third consistency parameters exceeds the first recognition threshold, it means that there is a high similarity between the third response speckle and the first standard speckle. Correspondingly, it can be determined that the user authentication passes at this time. On the contrary, when each third consistency parameter does not exceed the first recognition threshold, it means that there are large differences between each third response speckle and the first standard speckle. Correspondingly, it can be determined that the user authentication fails at this time.

[0167] S210: After the third user identification fails, lower the first recognition threshold to the second recognition threshold.

[0168] S211: Compare the third consistency parameter with the second recognition threshold to perform a fourth user identification.

[0169] S212: After the fourth user identification fails, based on the feature point matching algorithm, determine the feature points of any one of the first response speckle, the second response speckle, and the third response speckle and the feature points of the corresponding standard speckle, and calculate the first similarity between the two sets of feature points.

[0170] S213: Compare the first similarity with the third recognition threshold to perform a fifth user identification.

[0171] S214: After the fifth user identification fails, perform biometric identification on the user.

[0172] S215: After biometric authentication is passed, obtain activation information representing that the user pulls out the PUF and reinserts it, and irradiate the user's PUF with the third excitation code according to the activation information to obtain a fourth response speckle.

[0173] S216: Determine the feature points of the fourth response speckle and the feature points of the third standard speckle corresponding to the third excitation code based on the feature point matching algorithm, and calculate the second similarity between the two sets of feature points.

[0174] S217: Compare the second similarity with the third recognition threshold for the sixth user recognition.

[0175] The above S210 to S217 are basically the same as S107 to S114 in the above embodiment. For detailed content, please refer to the above embodiment and will not be elaborated here.

[0176] A user recognition method based on optical PUF authentication provided by an embodiment of the present invention further verifies the user's PUF through more verification methods to improve the repeatability of user recognition.

[0177] Specific content of a user recognition method based on optical PUF authentication provided by the present invention will be introduced in detail in the following embodiments of the invention and will not be elaborated here.

[0178] Embodiment III

[0179] Please refer to Figure 3 , Figure 3 which is a flowchart of another specific user recognition method based on optical PUF authentication provided by an embodiment of the present invention.

[0180] The method provided in this embodiment is usually executed after the above sixth user recognition is completed. If the sixth user recognition fails to pass the verification, the fourth response speckle needs to be corrected in this embodiment.

[0181] See Figure 3 , in this embodiment, the user recognition method based on optical PUF authentication includes:

[0182] S301: Determine the set of feature points that match each other between the set of feature points of the fourth response speckle and the set of feature points of the third standard speckle.

[0183] In this step, first, a set of feature points that match between the fourth response speckle and the third standard speckle is determined based on the feature point matching algorithm. When detecting feature points in this step, usually, a set of feature points of the fourth response speckle and a set of feature points of the third standard speckle are obtained first. When performing feature point matching, by comparison, similar feature point pairs in the two images are found to form a set of mutually matching feature points. That is, similar feature points are found from the two sets of feature points to form feature point pairs, and the two feature points that make up the feature point pair are the set of mutually matching feature points in the fourth response speckle and the third standard speckle. The specific content of the feature point matching algorithm has been introduced in detail in the above embodiments and will not be elaborated here.

[0184] S302: Obtain a first coordinate value group of the set of matching feature points in the fourth response speckle, and a second coordinate value group of the set of matching feature points in the third standard speckle.

[0185] In this step, the coordinate values of each feature point in its respective speckle image are determined. Of course, for the fourth response speckle and the third standard speckle, a coordinate system needs to be established according to the same standard, such as establishing an xy coordinate system with the first pixel point in the lower left corner as the origin, etc., which is not specifically limited here.

[0186] In this step, the coordinates of the mutually matching feature points in their respective speckle images need to be recorded. Denote the coordinate values of the above-mentioned feature points in the fourth response speckle as the first coordinate values to form a first coordinate value group. Each first coordinate value in this first coordinate value group can represent the position of each feature point in the feature point set in the fourth response speckle; denote the coordinate values of the above-mentioned feature points in the third standard speckle as the second coordinate values to form a second coordinate value group. Each second coordinate value in this second coordinate value group can represent the position of each feature point in the feature point set in the third standard speckle. For example, assume that a feature point is detected and matched in the fourth response speckle, and its coordinate value is (100, 200), while the coordinate value of the corresponding matching feature point in the third standard speckle is (95, 195).

[0187] S303: Determine the average displacement according to the first coordinate value group and the second coordinate value group.

[0188] The above displacement mean value is the value determined by taking the mean of the differences between the coordinate values of the mutually matching feature point pairs in their respective images, which characterizes the overall offset degree of the fourth response speckle relative to the third standard speckle. In this step, the displacement value corresponding to each feature point will be calculated based on each first coordinate value in the first coordinate value group and the second coordinate value corresponding to the mutually matching feature point in the second coordinate value group obtained above, and finally the displacement mean value will be calculated based on multiple displacement values. Specifically, for each pair of mutually matching feature points, the difference between its coordinate value in the fourth response speckle and its coordinate value in the third standard speckle can be calculated, that is, the displacement vector. Taking the xy coordinate system as an example, the displacement vector can be expressed as (Δx, Δy), where Δx is the displacement difference in the horizontal coordinate direction and Δy is the displacement difference in the vertical coordinate direction.

[0189] Then, the displacement vectors of all mutually matching feature points are averaged to obtain the displacement mean value. The displacement mean value reflects the overall offset degree of the fourth response speckle relative to the third standard speckle, and in the xy coordinate system, it usually includes the average offset amounts in the horizontal coordinate direction and the vertical coordinate direction.

[0190] For example, assume there are two pairs of mutually matching feature points. The displacement vector of the first pair of feature points is (5, 3), and the displacement vector of the second pair of feature points is (3, 5). Then the displacement mean value is ((5 + 3) / 2, (3 + 5) / 2), that is, (4, 4).

[0191] S304: Translate all pixel points in the fourth response speckle to the target coordinate points according to the displacement mean value to obtain the fifth response speckle.

[0192] In this embodiment, the fifth response speckle and the fourth response speckle have an overlapping area. The essence of this step is to correct the fourth response speckle. Therefore, in this step, each pixel point in the fourth response speckle will be translated from the current coordinate point to the target coordinate point according to the above displacement mean value, or in other words, the pixel value of the target coordinate point will be replaced with the pixel value corresponding to the current coordinate point, so as to obtain the fifth response speckle. Obviously, in this embodiment, the fifth response speckle and the fourth response speckle usually share the coordinate system. The image of the fifth response speckle is similar to that of the fourth response speckle but is translated as a whole. In this embodiment, after translation, the fifth response speckle and the fourth response speckle have overlapping areas and non-overlapping areas in terms of coordinates.

[0193] For example, assume that there is a pixel in the fourth response speckle with coordinates (150, 250), a pixel value of 128, and a displacement mean of (4, 4). Then the target coordinate value of this pixel can be (150 + 4, 250 + 4), that is, (154, 254). Correspondingly, the pixel value of the pixel located at (154, 254) in the fifth response speckle is 128. In this step, by performing translational correction on the fourth response speckle, the image features of the fifth response speckle and the third standard speckle in the overlapping area are made more consistent, which is beneficial to improving the robustness of the subsequent authentication process.

[0194] S305: Authenticate based on the picture of the fifth response speckle located in the overlapping area and the picture of the third standard speckle located in the corresponding area to perform the seventh user identification on the user.

[0195] The picture of the above-mentioned third standard speckle located in the corresponding area has the same coordinates as the picture of the fifth response speckle located in the overlapping area. That is, in this step, the picture of the fifth response speckle located in the above-mentioned overlapping area will be used to authenticate with the partial picture of the third standard speckle whose coordinates are the same as those of the above-mentioned overlapping area to perform the seventh user identification on the user. The specific process of authentication will be introduced in detail in the following embodiments of the invention and will not be elaborated here.

[0196] A user identification method based on optical PUF authentication provided by an embodiment of the present invention can determine the corresponding positions between the response speckle and the standard speckle through feature point matching, and determine the offset degree of the response speckle by calculating the displacement mean. Then, the response speckle is corrected first, and then user identification is performed based on the corrected response speckle and the third standard speckle, which can greatly improve the robustness of user identification.

[0197] The specific content of a user identification method based on optical PUF authentication provided by the present invention will be introduced in detail in the following embodiments of the invention and will not be elaborated here.

[0198] Embodiment 4

[0199] Please refer to Figure 4 and Figure 5 , Figure 4 which is a flowchart of another specific user identification method based on optical PUF authentication provided by an embodiment of the present invention; Figure 5 is the schematic diagram of the generation principle of the fifth response speckle.

[0200] See Figure 4 , in an embodiment of the present invention, the user identification method based on optical PUF authentication includes:

[0201] S401: Detect the feature point sets of the fourth response speckle and the third standard speckle based on the feature point matching algorithm.

[0202] In this step, all the feature points of the fourth response speckle will be detected to form a feature point set, and all the feature points of the third standard speckle will be detected to form a feature point set.

[0203] S402: Extract the feature points detected in the fourth response speckle and the feature point set detected in the third standard speckle.

[0204] In this step, the descriptors corresponding to each feature point in the feature point set detected in the fourth response speckle will be extracted, and the descriptors corresponding to each feature point in the feature point set detected in the third standard speckle will be extracted.

[0205] S403: Match the feature point set extracted from the fourth response speckle with the feature point set extracted from the third standard speckle, and screen out the mutually matching feature point sets.

[0206] The specific content of the above feature point matching algorithm can be set according to the actual situation and will not be specifically limited here. In this step, each feature point in the two extracted feature point sets will be matched. For example, by comparing the descriptors of the feature points extracted from different speckle images, the mutually matching feature points in the two sets of feature points will be screened out to form a mutually matching feature point set.

[0207] After this step, it is necessary to record the coordinates of the mutually matching feature points in their respective speckle images. The specific content has been introduced in detail in the above embodiments and will not be elaborated here. In this embodiment, assume that the feature point set in the fourth response speckle is R, and the feature point set R includes each feature point R 1 、R 2 ……R k , then record the first coordinate values of each feature point in the feature point set R as (M 1 ,N 1 ), (M 2 ,N 2 )……(M k ,N k ) to form a first coordinate value group; assume that the feature point set in the third standard speckle is S, and the feature point set S includes each feature point S 1 、S 2 ……S k , then record the second coordinate values of each feature point in the feature point set S as (P 1 ,Q 1 ), (P 2 ,Q 2 )……(P k ,Q k ) to form a second coordinate value group.

[0208] S404: Determine the mean horizontal displacement and the mean vertical displacement according to the first set of coordinate values and the second set of coordinate values.

[0209] In this step, the mean horizontal displacement m1 of each feature point in the feature point set R relative to each feature point in the feature point set S, and the mean vertical displacement m2 will be calculated, where:

[0210] m1 = [(P 1 - M 1 ) + (P 2 - M 2 ) + (P 3 - M 3 ) +...... + (P k - M k )] / k;

[0211] m2 = [(Q 1 - N 1 ) + (Q 2 - N 2 ) + (Q 3 - N 3 ) +...... + (Q k - N k )] / k.

[0212] Correspondingly, in subsequent steps, all pixel points in the fourth response speckle will be translated to target coordinate points according to the mean horizontal displacement and the mean vertical displacement to form a fifth response speckle. For example, for the pixel point (i, j) in the fourth response speckle, its corresponding target coordinate point is (i + m1, j + m2), that is, the pixel value of the pixel point (i, j) in the fourth response speckle is the same as the pixel value of the pixel point (i + m1, j + m2) in the fifth response speckle.

[0213] S405: Generate a blank picture with the same size as the fourth response speckle.

[0214] In this embodiment, the pixel value of the pixel point in the blank picture is 0. That is, in this step, a picture with all pixel points being 0 will be generated first, and the size of this blank picture is usually the same as that of the fourth response speckle.

[0215] S406: Determine the target coordinate values corresponding to each pixel point in the fourth response speckle according to the displacement mean and the current coordinate values of each pixel point in the fourth response speckle.

[0216] See Figure 5, in this step, the displacement mean value calculated in the above step is added to the current coordinate values of each pixel point in the fourth response speckle to obtain the target coordinate value corresponding to each pixel point. For example, for the pixel point (i, j) in the fourth response speckle, its corresponding target coordinate point is (i + m1, j + m2).

[0217] S407: Fill the pixel values of each pixel point in the fourth response speckle into the pixel points with corresponding target coordinate values in the blank picture to obtain the fifth response speckle.

[0218] In this step, the pixel value V of the pixel point (i, j) in the fourth response speckle ij is filled into the pixel point (i + m1, j + m2) in the blank picture, so that the pixel value of the pixel point (i + m1, j + m2) in the blank picture is also V ij , thereby obtaining the fifth response speckle.

[0219] S408: Based on the displacement mean value, crop the fifth response speckle and the third standard speckle to obtain the final response speckle that does not exceed the overlapping area, and the final standard speckle whose position corresponds to the final response speckle.

[0220] In this step, the fifth response speckle and the third standard speckle are cropped, and the above displacement mean value needs to be specifically referred to during cropping. When translating the fourth response speckle to obtain the fifth response speckle, there is usually an overlapping area between the fourth response speckle and the fifth response speckle in the same coordinate system. In this step, the fifth response speckle needs to be cropped so that the coordinates corresponding to each pixel point in the final response speckle do not exceed the range of the above overlapping area. For example, when cropping the fifth response speckle, only the pixel area with the abscissa range (1 + m1, i - m1) and the ordinate range (1 + m2, j - m2) is retained. This pixel area does not exceed the above overlapping area, so that the cropped final response speckle does not exceed the above overlapping area. Correspondingly, in this step, the third standard speckle also needs to be cropped to obtain the final standard speckle. The pixel area corresponding to the final standard speckle needs to correspond to the pixel area of the final response speckle. Therefore, when cropping, only the pixel area with the abscissa range (1 + m1, i - m1) and the ordinate range (1 + m2, j - m2) can also be retained to obtain the final standard speckle. Of course, in this embodiment, the cropping area is not specifically limited. For example, it can be further reduced based on the above coordinate range, which is not specifically limited here. Correspondingly, in subsequent steps, authentication needs to be performed according to the final response speckle and the final standard speckle.

[0221] S409: Based on the image filtering algorithm, determine the sixth binary texture feature picture corresponding to the final response speckle and the seventh binary texture feature picture corresponding to the final standard speckle.

[0222] In this step, image filtering algorithms such as the Gabor filtering algorithm can be used to process the above two speckle images. The specific content of image filtering algorithms such as the Gabor filtering algorithm can be referred to the prior art and will not be elaborated here. In this step, the final response speckle and the final standard speckle can be processed respectively based on the Gabor filtering algorithm, so as to obtain the sixth binary texture feature image corresponding to the final response speckle and the seventh binary texture feature image corresponding to the final standard speckle.

[0223] S410: Determine the fourth consistency parameter between the sixth binary texture feature image and the seventh binary texture feature image.

[0224] In this step, the consistency parameter between the above sixth binary texture feature image and the seventh binary texture feature image can be determined as the fourth consistency parameter, which is used to characterize the consistency degree or similarity degree between the sixth binary texture feature image and the seventh binary texture feature image. The first parameter can be the Pearson correlation coefficient or the Hamming distance, etc. The specific calculation process of the Hamming distance or the Pearson correlation coefficient can be referred to the prior art and will not be elaborated here. The fourth consistency parameter can characterize the difference degree between the sixth binary texture feature image and the seventh binary texture feature image.

[0225] S411: Determine whether the user authentication passes according to the fourth consistency parameter.

[0226] Taking the Hamming distance as an example, the smaller the Hamming distance, the higher the similarity between the two images. In this step, when the fourth Hamming distance is less than the target threshold, it means that there is a high similarity between the fourth response speckle and the third standard speckle, and correspondingly, it can be determined that the user authentication passes at this time. On the contrary, when the fourth Hamming distance is greater than or equal to the target threshold, it means that there is a large difference between the fourth response speckle and the third standard speckle, and correspondingly, it can be determined that the user authentication fails at this time. Taking the Pearson correlation coefficient as an example, the larger the Pearson correlation coefficient, the higher the similarity between the two images. In this step, when the Pearson correlation coefficient is greater than the target threshold, it means that there is a high similarity between the first response speckle and the standard speckle, and correspondingly, it can be determined that the user authentication passes at this time. On the contrary, when the Pearson correlation coefficient is less than or equal to the target threshold, it means that there is a large difference between the first response speckle and the standard speckle, and correspondingly, it can be determined that the user authentication fails at this time.

[0227] The above target threshold needs to be determined according to the type of the consistency parameter. For example, corresponding to the Hamming distance, the target threshold can be 0.3; corresponding to the Pearson correlation coefficient, the target threshold can be 0.7. The specific value of the above target threshold can be set according to the actual situation and will not be specifically limited here.

[0228] The user identification method based on optical PUF authentication provided by the embodiments of the present invention can effectively solve the problem of authentication speckle offset caused by factors such as unstable authentication systems and repeated plugging and unplugging of the PUF by users during the optical PUF authentication process, improve the stability and robustness of the optical PUF authentication system, ensure that legitimate users can pass the authentication smoothly, prevent illegal access by illegal users at the same time, and enhance the security of the system.

[0229] Embodiment Five

[0230] The following introduces a user identification device based on optical PUF authentication provided by the embodiments of the present invention. The user identification device based on optical PUF authentication described below can be referred to corresponding to the user identification method based on optical PUF authentication described above.

[0231] Please refer to Figure 6 , Figure 6 which is a structural block diagram of a user identification device based on optical PUF authentication provided by the embodiments of the present invention.

[0232] Refer to Figure 6 , in the embodiments of the present invention, the user identification device based on optical PUF authentication may include:

[0233] The first response speckle module 100 is configured to irradiate the user PUF with the first excitation code to obtain a first response speckle;

[0234] The first user identification module 200 is configured to determine a first consistency parameter between the first response speckle and a first standard speckle corresponding to the first excitation code, and perform a first user identification according to the first consistency parameter and a first identification threshold;

[0235] The second response speckle module 300 is configured to, after the first user identification fails, irradiate the user PUF again with the same first excitation code to continuously obtain a plurality of second response speckles;

[0236] The second user identification module 400 is configured to determine a second consistency parameter between each of the second response speckles and the first standard speckle, and perform a second user identification according to the plurality of second consistency parameters and the first identification threshold;

[0237] The third response speckle module 500 is configured to, after the second user identification fails, irradiate the user PUF with a plurality of different second excitation codes in sequence, and obtain a third response speckle after each irradiation of the user PUF with a second excitation code; each of the second excitation codes corresponds to a second standard speckle;

[0238] The third user identification module 600 is used to determine the third consistency parameter between each of the third response speckles and the second standard speckle corresponding to the second excitation code, so as to perform the third user identification according to the multiple third consistency parameters and the first identification threshold;

[0239] The threshold reduction module 700 is used to reduce the first identification threshold to the second identification threshold after the third user identification fails;

[0240] The fourth user identification module 800 is used to compare the third consistency parameter with the second identification threshold to perform the fourth user identification;

[0241] The first similarity module 900 is used to, after the fourth user identification fails, determine the feature point set of any one of the first response speckle, the second response speckle, and the third response speckle and the feature point set of the corresponding standard speckle based on the feature point matching algorithm, and calculate the first similarity between the two groups of feature point sets;

[0242] The fifth user identification module 1000 is used to compare the first similarity with the third identification threshold to perform the fifth user identification;

[0243] The biometric identification module 1100 is used to perform biometric identification on the user after the fifth user identification fails;

[0244] The fourth response speckle module 1200 is used to, after the biometric identification passes, obtain the activation information indicating that the user pulls out the PUF and reinserts it, and irradiate the user PUF with the third excitation code according to the activation information to obtain the fourth response speckle;

[0245] The second similarity module 1300 is used to determine the feature point set of the fourth response speckle and the feature point set of the third standard speckle corresponding to the third excitation code based on the feature point matching algorithm, and calculate the second similarity between the two groups of feature point sets;

[0246] The sixth user identification module 1400 is used to compare the second similarity with the third identification threshold to perform the sixth user identification.

[0247] Preferably, in the embodiment of the present invention, it further includes:

[0248] The feature point matching module is used to determine the feature point set that matches each other between the feature point set of the fourth response speckle and the feature point set of the third standard speckle;

[0249] The coordinate value module is used to obtain the first coordinate value group of the matching feature point set in the fourth response speckle, and the second coordinate value group of the matching feature point set in the third standard speckle;

[0250] A displacement mean value module, configured to determine a displacement mean value according to the first coordinate value group and the second coordinate value group;

[0251] A fifth response speckle module, configured to translate all pixel points in the fourth response speckle to target coordinate points according to the displacement mean value to obtain a fifth response speckle; there is an overlapping area between the fifth response speckle and the fourth response speckle;

[0252] A seventh user identification module, configured to perform authentication based on a picture of the fifth response speckle located in the overlapping area and a picture of the third standard speckle located in the corresponding area to perform a seventh user identification on the user.

[0253] Preferably, in an embodiment of the present invention, the first similarity module includes:

[0254] A first quantity unit, configured to determine the quantity of feature points that match each other between two sets of feature point sets;

[0255] A first similarity unit, configured to determine the percentage of the quantity of feature points that match each other in the total quantity of feature points as the first similarity;

[0256] And / or, the second similarity module includes:

[0257] A second quantity unit, configured to determine the quantity of feature points that match each other between two sets of feature point sets;

[0258] A second similarity unit, configured to determine the percentage of the quantity of feature points that match each other in the total quantity of feature points as the second similarity.

[0259] Preferably, in an embodiment of the present invention, the second user identification module is specifically configured to:

[0260] When any one of the second consistency parameters exceeds the first identification threshold, it is determined that the second user identification is passed;

[0261] And / or, the third user identification module is specifically configured to:

[0262] When any one of the third consistency parameters exceeds the first identification threshold, it is determined that the third user identification is passed;

[0263] And / or, the fourth user identification module is specifically configured to:

[0264] When any one of the third consistency parameters exceeds the second identification threshold, it is determined that the fourth user identification is passed.

[0265] Preferably, in an embodiment of the present invention, the first user identification module is specifically configured to:

[0266] Determine the Pearson correlation coefficient between the first response speckle and the first standard speckle corresponding to the first excitation code as the first consistency parameter;

[0267] And / or, the second user recognition module is used for:

[0268] Determine the Pearson correlation coefficient between each second response speckle and the first standard speckle as the second consistency parameter;

[0269] And / or, the third user recognition module is specifically used for:

[0270] Determine the Pearson correlation coefficient between each third response speckle and the second standard speckle corresponding to the second excitation code as the third consistency parameter.

[0271] Preferably, in the embodiment of the present invention, the first user recognition module includes:

[0272] A first filtering unit, configured to determine a first binary texture feature picture of the first response speckle and a second binary texture feature picture of the first standard speckle based on an image filtering algorithm;

[0273] A first consistency parameter unit, configured to determine a first consistency parameter between the first binary texture feature picture and the second binary texture feature picture;

[0274] And / or, the second user recognition module includes:

[0275] A second filtering unit, configured to determine a third binary texture feature picture of each second response speckle and a second binary texture feature picture of the first standard speckle based on an image filtering algorithm;

[0276] A second consistency parameter unit, configured to determine a second consistency parameter between the second binary texture feature picture and each third binary texture feature picture;

[0277] And / or, the third user recognition module includes:

[0278] A third filtering unit, configured to determine a fourth binary texture feature picture of each third response speckle and a fifth binary texture feature picture of each second standard speckle based on an image filtering algorithm;

[0279] A third consistency parameter unit, configured to determine a third consistency parameter between each fourth binary texture feature picture and the corresponding fifth binary texture feature picture.

[0280] Preferably, in the embodiment of the present invention, the first consistency parameter unit is specifically used for:

[0281] Determine the Hamming distance between the first binarized texture feature picture and the second binarized texture feature picture as the first consistency parameter;

[0282] Or, determine the Pearson correlation coefficient between the first binarized texture feature picture and the second binarized texture feature picture as the first consistency parameter;

[0283] The second consistency parameter unit is specifically configured to:

[0284] Determine the Hamming distance between the second binarized texture feature picture and each of the third binarized texture feature pictures as the second consistency parameter;

[0285] Or, determine the Pearson correlation coefficient between the second binarized texture feature picture and each of the third binarized texture feature pictures as the second consistency parameter;

[0286] The third consistency parameter unit is specifically configured to:

[0287] Determine the Hamming distance between each of the fourth binarized texture feature pictures and the corresponding fifth binarized texture feature picture as the third consistency parameter;

[0288] Or, determine the Pearson correlation coefficient between each of the fourth binarized texture feature pictures and the corresponding fifth binarized texture feature picture as the third consistency parameter.

[0289] The user identification device based on optical PUF authentication in this embodiment is used to implement the foregoing user identification method based on optical PUF authentication. Therefore, the specific implementation manners in the user identification device based on optical PUF authentication can be seen in the embodiment part of the user identification method based on optical PUF authentication in the foregoing text. For example, the first response speckle module 100, the first user identification module 200, the second response speckle module 300, the second user identification module 400, the third response speckle module 500, the third user identification module 600, the threshold down - adjustment module 700, the fourth user identification module 800, the first similarity module 900, the fifth user identification module 1000, the biometric identification module 1100, the fourth response speckle module 1200, the second similarity module 1300, and the sixth user identification module 1400 are respectively used to implement steps S101 to S114 in the foregoing user identification method based on optical PUF authentication. Therefore, the specific implementation manners can refer to the descriptions of the corresponding respective part embodiments and will not be elaborated herein.

[0290] Embodiment Six

[0291] The following introduces a user identification device based on optical PUF authentication provided by an embodiment of the present invention. The user identification device based on optical PUF authentication described below can be correspondingly referred to the user identification method based on optical PUF authentication and the user identification device based on optical PUF authentication described above.

[0292] Please refer to Figure 7 , Figure 7 which is a structural block diagram of a user identification device based on optical PUF authentication provided by an embodiment of the present invention.

[0293] Referring to Figure 7 , the user identification device based on optical PUF authentication may include a processor 11 and a memory 12.

[0294] The memory 12 is used to store a computer program; the processor 11 is used to implement the user identification method based on optical PUF authentication described in the above-mentioned invention embodiment when executing the computer program.

[0295] In the user identification device based on optical PUF authentication of this embodiment, the processor 11 is used to install the user identification device based on optical PUF authentication described in the above-mentioned invention embodiment. At the same time, the combination of the processor 11 and the memory 12 can implement the user identification method based on optical PUF authentication described in any of the above-mentioned invention embodiments. Therefore, for the specific implementation manners in the user identification device based on optical PUF authentication, reference can be made to the embodiment part of the user identification method based on optical PUF authentication in the foregoing text. Its specific implementation manners can be referred to the descriptions of the corresponding various part embodiments and will not be elaborated herein.

[0296] Embodiment Seven

[0297] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a user identification method based on optical PUF authentication introduced in any of the above-mentioned invention embodiments. The remaining content can be referred to the prior art and will not be further described herein.

[0298] In this specification, the various embodiments are described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple. For the relevant parts, reference can be made to the description in the method part.

[0299] Those skilled in the art may further realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to implement the described functions, but such implementation should not be considered as exceeding the scope of the present invention.

[0300] The steps of the methods or algorithms described in combination with the embodiments disclosed herein can be directly implemented by hardware, software modules executed by a processor, or a combination of both. The software modules can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the technical field.

[0301] Finally, it should also be noted that in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0302] The above has introduced in detail a user identification method and related device based on optical PUF authentication provided by the present invention. Specific examples are used herein to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A user identification method based on optical PUF authentication, characterized in that: include: Irradiate the user PUF with a first excitation code to obtain a first response speckle; determining a first consistency parameter between the first response speckle and a first standard speckle corresponding to the first excitation code, so as to perform a first user identification according to the first consistency parameter and a first identification threshold; After the first user identification fails, the user PUF is re-illuminated with the same first excitation code to continuously obtain a plurality of second response speckles; determining a second consistency parameter between each of the second response speckles and the first standard speckle, so as to perform a second user identification according to the plurality of the second consistency parameters and the first identification threshold; After the second user identification fails, a plurality of different second excitation codes are used to sequentially illuminate the user PUF, and after each second excitation code is used to illuminate the user PUF, a third response speckle is obtained; each of the second excitation codes corresponds to a second standard speckle; determining a third consistency parameter between each of the third response speckles and a second standard speckle corresponding to the second excitation code, so as to perform a third user identification according to the plurality of the third consistency parameters and the first identification threshold; After the third user identification fails, lowering the first identification threshold to a second identification threshold; comparing the third consistency parameter with the second identification threshold to perform a fourth user identification; After the fourth user identification fails, determining a feature point set of any one of the first response speckle, the second response speckle, and the third response speckle and a feature point set of a corresponding standard speckle based on a feature point matching algorithm, and calculating a first similarity between the two sets of feature point sets; Comparing the first similarity with a third identification threshold to perform a fifth user identification; After the user identification fails for the fifth time, the user is subjected to biometric identification; When the biometric identification is passed, activation information indicating that the user has unplugged the PUF and reinserted it is obtained, and the user PUF is illuminated with a third excitation code according to the activation information to obtain a fourth response speckle; Determine a feature point set of the fourth response speckle and a feature point set of the third standard speckle corresponding to the third excitation code based on a feature point matching algorithm, and calculate a second similarity between the two sets of feature point sets; The second similarity is compared with the third identification threshold to perform a sixth user identification.

2. The method according to claim 1, characterized in that After determining the feature point set of the fourth response speckle and the feature point set of the third standard speckle corresponding to the third excitation code based on the feature point matching algorithm, the method further includes: Determine a feature point set that matches the feature point set of the fourth response speckle and the feature point set of the third standard speckle; Acquire a first coordinate value group of a feature point set matched in the fourth response speckle and a second coordinate value group of a feature point set matched in the third standard speckle; Determine a displacement mean value according to the first coordinate value group and the second coordinate value group; All pixel points in the fourth response speckle are translated to the target coordinate point according to the displacement mean value to obtain a fifth response speckle; the fifth response speckle has an overlapping area with the fourth response speckle; Authentication is performed based on the picture in which the fifth response speckle is located in the overlapping area and the picture in which the third standard speckle is located in the corresponding area, so as to perform the seventh user identification on the user.

3. The method according to claim 1, characterized in that Calculating the first similarity between two sets of feature point sets includes: Determine the number of feature points that match each other between two sets of feature point sets; Determine the percentage of the number of mutually matching feature points to the total number of feature points as the first similarity; And / or, calculating the second similarity between the two sets of feature point sets includes: Determine the number of feature points that match each other between two sets of feature point sets; The percentage of the number of mutually matching feature points to the total number of feature points is determined as the second similarity.

4. The method according to claim 1, characterized in that Performing a second user identification according to the plurality of the second consistency parameters and the first identification threshold comprises: When any of the second consistency parameters exceeds the first identification threshold, it is determined that the second user identification is successful; And / or, performing a third user identification according to the plurality of third consistency parameters and the first identification threshold comprises: When any of the third consistency parameters exceeds the first identification threshold, it is determined that the third user identification is successful; And / or, performing a fourth user identification includes: When any one of the third consistency parameters exceeds the second identification threshold, it is determined that the fourth user identification is successful.

5. The method according to claim 1, characterized in that Determining a first consistency parameter between the first response speckle and a first standard speckle corresponding to the first excitation code includes: determining a Pearson correlation coefficient between the first response speckle and a first standard speckle corresponding to the first excitation code as the first consistency parameter; And / or, determining a second consistency parameter between each of the second response speckles and the first standard speckle comprises: determining a Pearson correlation coefficient between each of the second response speckles and the first standard speckle as the second consistency parameter; And / or, determining a third consistency parameter between each of the third response speckles and a second standard speckle corresponding to the second excitation code comprises: A Pearson correlation coefficient between each of the third response speckles and a second standard speckle corresponding to the second excitation code is determined as the third consistency parameter.

6. The method according to claim 1, characterized in that Determining a first consistency parameter between the first response speckle and a first standard speckle corresponding to the first excitation code includes: Determining a first binary texture feature picture of the first response speckle and a second binary texture feature picture of the first standard speckle based on an image filtering algorithm; Determining a first consistency parameter between the first binarized texture feature picture and the second binarized texture feature picture; And / or, determining a second consistency parameter between each of the second response speckles and the first standard speckle comprises: Determining, based on an image filtering algorithm, a third binary texture feature image of each of the second response speckles and a second binary texture feature image of the first standard speckle; Determining a second consistency parameter between the second binarized texture feature picture and each of the third binarized texture feature pictures; And / or, determining a third consistency parameter between each of the third response speckles and a second standard speckle corresponding to the second excitation code comprises: Determining, based on an image filtering algorithm, a fourth binary texture feature image of each of the third response speckles and a fifth binary texture feature image of each of the second standard speckles; Determine a third consistency parameter between each of the fourth binarized texture feature pictures and the corresponding fifth binarized texture feature picture.

7. The method according to claim 6, characterized in that Determining a first consistency parameter between the first binary texture feature picture and the second binary texture feature picture includes: Determine a Hamming distance between the first binary texture feature picture and the second binary texture feature picture as the first consistency parameter; or, determining a Pearson correlation coefficient between the first binarized texture feature picture and the second binarized texture feature picture as the first consistency parameter; Determining a second consistency parameter between the second binary texture feature picture and each of the third binary texture feature pictures includes: Determine a Hamming distance between the second binarized texture feature picture and each of the third binarized texture feature pictures as the second consistency parameter; or, determining a Pearson correlation coefficient between the second binarized texture feature picture and each of the third binarized texture feature pictures as the second consistency parameter; Determining a third consistency parameter between each of the fourth binarized texture feature pictures and the corresponding fifth binarized texture feature picture includes: Determine a Hamming distance between each of the fourth binarized texture feature pictures and the corresponding fifth binarized texture feature picture as the third consistency parameter; Or, determine the Pearson correlation coefficient between each of the fourth binarized texture feature pictures and the corresponding fifth binarized texture feature picture as the third consistency parameter.

8. A user identification device based on optical PUF authentication, characterized in that: include: A first response speckle module, used to illuminate the user PUF with a first excitation code to obtain a first response speckle; a first user identification module, configured to determine a first consistency parameter between the first response speckle and a first standard speckle corresponding to the first excitation code, so as to perform a first user identification according to the first consistency parameter and a first identification threshold; A second response speckle module is used to re-irradiate the user PUF with the same first excitation code after the first user identification fails, and continuously obtain a plurality of second response speckles; a second user identification module, used for determining a second consistency parameter between each of the second response speckles and the first standard speckle, so as to perform a second user identification according to the plurality of the second consistency parameters and the first identification threshold; A third response speckle module is used to use a plurality of different second excitation codes to sequentially illuminate the user PUF after the second user identification fails, and obtain a third response speckle after each second excitation code is used to illuminate the user PUF; each of the second excitation codes corresponds to a second standard speckle; a third user identification module, configured to determine a third consistency parameter between each of the third response speckles and a second standard speckle corresponding to the second excitation code, so as to perform a third user identification according to the plurality of the third consistency parameters and the first identification threshold; A threshold down-adjusting module, configured to down-adjust the first recognition threshold to a second recognition threshold after the user recognition fails for the third time; a fourth user identification module, configured to compare the third consistency parameter with the second identification threshold to perform a fourth user identification; A first similarity module is used to determine, after the fourth user identification fails, feature points of any one of the first response speckle, the second response speckle, and the third response speckle and feature points of the corresponding standard speckle based on a feature point matching algorithm, and calculate a first similarity between the two groups of feature points; a fifth user identification module, configured to compare the first similarity with a third identification threshold to perform a fifth user identification; A biometric identification module, used for performing biometric identification on the user after the user identification fails for the fifth time; The fourth response speckle module is used to obtain activation information indicating that the user has unplugged the PUF and reinserted it after the biometric identification is passed, and to illuminate the user PUF with a third excitation code according to the activation information to obtain a fourth response speckle; A second similarity module, configured to determine the feature points of the fourth response speckle and the feature points of the third standard speckle corresponding to the third excitation code based on a feature point matching algorithm, and calculate a second similarity between the two groups of feature points; The sixth user identification module is used to compare the second similarity with the third identification threshold to perform a sixth user identification.

9. A user identification device based on optical PUF authentication, characterized in that: The device comprises: Memory: used to store computer programs; Processor: configured to implement the steps of the user identification method based on optical PUF authentication as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the user identification method based on optical PUF authentication according to any one of claims 1 to 7 are implemented.

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