Facial image updating method, storage medium, electronic device and vehicle

Abstract

The present invention discloses a method for updating a facial image, a storage medium, an electronic device, and a vehicle. The method for updating a facial image includes: collecting a facial image to be identified; evaluating the facial image to be identified to obtain a first quality score, and extracting features of the facial image to be identified to obtain a first image feature; obtaining pre-stored image features of a pre-stored standard facial image; and determining whether to update the pre-stored image features to the first image features based on the first quality score, the first image features, and the pre-stored image features. The method for updating a facial image can achieve real-time updating of a standard facial image, thereby reducing the impact of facial variability on the facial recognition system and improving the accuracy and usability of facial recognition.

Description

Technical Field

[0001] The present invention relates to the field of image recognition technology, and in particular to a method for updating a face image, a storage medium, an electronic device and a vehicle. Background Art

[0002] At present, face recognition technology is increasingly used in the automotive field, such as external face unlocking systems, internal driver fatigue detection, driver identity recognition, etc. In the application of face recognition technology, it is necessary to ensure the security of face recognition and the ease of use of the face recognition system. Since the in-vehicle face unlocking system has a long service life, it is necessary to update the standard face features to avoid the situation where the face images of the same individual in different time periods cannot be matched due to the volatility of the face, the influence of factors such as lighting, age, and fatness.

[0003] To solve the above problems, the related art proposes a technology that updates the standard facial features based on the interval between two face unlocks. However, this technology needs to be associated with time, and is not applicable to situations where the user has not unlocked for a long time and the face has changed significantly, such as short-term weight gain or weight loss. At the same time, there is no unified standard for the preset unlocking time between two times, which is not friendly to the system usability. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the first object of the present invention is to propose a method for updating a face image, which can realize real-time updating of a standard face image, thereby reducing the influence of the face variability on the face recognition system and improving the accuracy and usability of face recognition.

[0005] A second object of the present invention is to provide a computer-readable storage medium.

[0006] A third objective of the present invention is to provide an electronic device.

[0007] A fourth object of the present invention is to provide a vehicle.

[0008] To achieve the above-mentioned purpose, the first aspect of the embodiment of the present invention proposes a method for updating a facial image, which includes the following steps: acquiring a facial image to be identified; evaluating the quality of the facial image to be identified to obtain a first quality score, and extracting features of the facial image to be identified to obtain a first image feature; acquiring pre-stored image features of a pre-stored standard facial image; and determining whether to update the pre-stored image features to the first image features based on the first quality score, the first image features and the pre-stored image features.

[0009] The method for updating a face image in an embodiment of the present invention performs a quality assessment on the face image to be identified when the face image to be identified is collected to obtain a first quality score, and performs feature extraction on the face image to be identified to obtain a first image feature; and then determines whether to update the pre-stored image feature to the first image feature based on the first quality score, the first image feature, and the pre-stored image feature. In this way, the standard face image can be updated in real time, thereby reducing the impact of the face variability on the face recognition system and improving the accuracy and usability of face recognition.

[0010] In addition, the above-mentioned method for updating a face image of the present invention may also have the following additional technical features:

[0011] According to one embodiment of the present invention, before extracting the features of the facial image to be identified to obtain the first image features, the facial image updating method also includes: determining whether the first quality score is within a first preset quality range; if the first quality score is within the first preset quality range, executing the step of extracting the features of the facial image to be identified to obtain the first image features.

[0012] According to one embodiment of the present invention, determining whether to update the pre-stored image feature to the first image feature based on the first quality score, the first image feature and the pre-stored image feature includes: calculating the similarity between the first image feature and the pre-stored image feature corresponding to the first standard face image to obtain a first similarity; determining whether the first similarity is greater than a first preset similarity threshold; if the first similarity is greater than the first preset similarity threshold, performing face unlocking, and determining whether the first quality score is within a second preset quality range, wherein the image quality corresponding to the second preset quality range is better than the image quality corresponding to the first preset quality range; if the first quality score is within the second preset quality range, updating the pre-stored image feature corresponding to the first standard face image to the first image feature; if the first quality score is not within the second preset quality range, updating the pre-stored image feature corresponding to the second standard face image to the first image feature.

[0013] According to one embodiment of the present invention, determining whether to update the pre-stored image feature to the first image feature based on the first quality score, the first image feature and the pre-stored image feature also includes: if the first similarity is less than or equal to the first preset similarity threshold, calculating the similarity between the first image feature and the pre-stored image feature corresponding to the second standard face image to obtain a second similarity; determining whether the second similarity is greater than a second preset similarity threshold; if the second similarity is greater than the second preset similarity threshold, performing face unlocking.

[0014] According to one embodiment of the present invention, the facial image updating method further includes: if the second similarity is less than or equal to the second preset similarity threshold, issuing a first prompt message to prompt that face unlocking has failed.

[0015] According to one embodiment of the present invention, the facial image updating method also includes: obtaining the consecutive number of face unlocking using the second standard facial image; if the consecutive number reaches a first preset number, updating the pre-stored image features corresponding to the current first standard facial image to the current first image features.

[0016] According to an embodiment of the present invention, the facial image updating method further includes: determining the second preset quality range according to the quality of the first standard facial image and the quality of the second standard facial image.

[0017] According to one embodiment of the present invention, the facial image updating method also includes: determining whether the acquisition device of the facial image to be identified is the same as that of the first standard facial image; determining the first preset similarity threshold based on the judgment result, wherein the first preset similarity threshold determined when the judgment results are the same is greater than the preset similarity threshold determined when the judgment results are different.

[0018] To achieve the above-mentioned purpose, the second aspect of the present invention provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned method for updating a facial image is implemented. .

[0019] The computer-readable storage medium of an embodiment of the present invention can realize real-time updating of standard facial images when the computer program corresponding to the above-mentioned facial image updating method stored thereon is executed, thereby reducing the impact of facial variability on the facial recognition system and improving the accuracy and ease of use of facial recognition.

[0020] To achieve the above-mentioned purpose, a third aspect of the present invention proposes an electronic device, comprising a memory, a processor and a computer program stored in the memory, wherein when the computer program is executed by the processor, the above-mentioned method for updating a facial image is implemented.

[0021] The electronic device of the embodiment of the present invention can realize real-time updating of standard face images by implementing the above-mentioned face image updating method, thereby reducing the influence of face variability on the face recognition system and improving the accuracy and ease of use of face recognition.

[0022] To achieve the above-mentioned object, a fourth aspect of the present invention provides a vehicle, comprising the above-mentioned electronic device.

[0023] The vehicle of the embodiment of the present invention can realize real-time updating of the standard facial image through the above-mentioned electronic device, thereby reducing the influence of the facial variability on the facial recognition system and improving the accuracy and ease of use of facial recognition.

[0024] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flow chart of a method for updating a face image according to a first embodiment of the present invention;

[0026] Figure 2 is a flow chart of a method for updating a face image according to a second embodiment of the present invention;

[0027] Figure 3 is a flow chart of a method for updating a face image according to a specific embodiment of the present invention;

[0028] Figure 4 4 is a structural block diagram of a vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0030] Please refer to the following Figure 1-4 A method for updating a facial image, an electronic device, and a vehicle according to an embodiment of the present invention are described.

[0031] Figure 1 The figure is a flow chart of a method for recognizing a facial image according to an embodiment of the present invention.

[0032] like Figure 1 As shown, the method for recognizing a face image includes the following steps:

[0033] S11, collecting a face image to be recognized.

[0034] As an example, Figure 3 As shown, a video stream of a face to be identified can be collected by an acquisition device (such as a camera on a vehicle terminal), and then a face image to be identified can be obtained according to the video stream of the face to be identified. The face image can be any one of a 2D image, a depth image and a 3D face image.

[0035] Specifically, obtaining a face image to be identified based on a face video stream to be identified may include: obtaining an image containing a face based on a face video stream to be identified, and then extracting a face region and locating key points on the image. For example, the image containing a face may be input into a preset face detection network to obtain a face region and key point locations, and then obtaining key point information based on the key point locations. The image in the face region may be used as a face image to be identified, and the key points may be 5 points or 68 points.

[0036] S12, evaluating the quality of the face image to be recognized to obtain a first quality score, and extracting features of the face image to be recognized to obtain first image features.

[0037] Specifically, the extracted face region is subjected to quality assessment to obtain a first quality score. For example, face alignment may be performed based on the key point information, and then the face region is subjected to quality assessment to obtain a first quality score.

[0038] Further, it is determined whether the first quality score is within a first preset quality range; if the first quality score is within the first preset quality range, the step of extracting features from the face image to be identified to obtain first image features is performed.

[0039] As an example, Figure 3 As shown, whether the first quality score is within the first preset quality range can be determined by comparing the relationship between the first quality score and the first quality threshold. If the first quality score indicates the quality of the face image, the higher the first quality score, the better the quality of the face image. At this time, to achieve that the first quality score is within the first preset quality range, the first quality score needs to be greater than the first quality threshold. If the first quality score indicates the quality loss of the face image, the higher the first quality score, the worse the quality of the face image. At this time, to achieve that the first quality score is within the first preset quality range, the first quality score needs to be less than the first quality threshold.

[0040] The first quality threshold may be set by the user. For example, the first quality threshold may be set as a percentage r1 of the maximum value that the first quality score may reach, where r1 is a value between 0 and 100%.

[0041] Optionally, if the first quality score is not within the first preset quality range, the step of acquiring the face image to be recognized is performed again.

[0042] In this way, it is possible to detect whether the quality of the face image to be identified is qualified. Specifically, if the above-mentioned first quality score is within the first preset quality range, it means that the quality of the face image to be identified corresponding to the first quality score is qualified, and the subsequent update steps can be continued; if the above-mentioned first quality score is not within the first preset quality range, it means that the quality of the face image to be identified corresponding to the first quality score is unqualified, and the image cannot realize face unlocking, and is not suitable for updating the standard image.

[0043] Furthermore, if the quality of the face image to be identified is qualified, feature extraction is performed on the face image to be identified to obtain a first image feature.

[0044] Specifically, feature extraction is performed on the face image to be recognized to obtain a feature matrix, and the feature matrix is ​​used as the first image feature.

[0045] Optionally, the face image to be identified can also be extracted to obtain features that can be used for face recognition. For example, at least one of the HOG (Histogram of Oriented Gradient) features, Haar features, color features, etc. of the face image to be identified can be extracted, and then the feature can be used as the first image feature.

[0046] It should be noted that after obtaining the first image feature according to the above-mentioned face image to be identified, the liveness feature of the above-mentioned face image to be identified can also be obtained. The liveness feature is a feature that describes whether the face included in the face image to be identified is a live face, and the liveness feature can include local texture features, light reflection features, biological motion features, etc. in the face image.

[0047] S13, obtaining pre-stored image features of pre-stored standard face images.

[0048] Specifically, the pre-stored image features can be stored in a template library for face recognition. The process of obtaining the pre-stored image features can be: extracting features from a pre-stored standard face image to obtain a feature matrix, and then the feature matrix can be used as the pre-stored image features. The pre-stored image features can be obtained when the pre-stored standard face image is obtained and pre-stored in the face recognition system, thereby improving the update processing efficiency; they can also be obtained by extracting features from a pre-stored standard face image when the first image feature is obtained.

[0049] It should be noted that the template library can be a database in the vehicle face recognition system, and the database can also contain pre-stored standard face images for face recognition unlocking, and there is a corresponding relationship between the pre-stored standard face images and their pre-stored image features. The above-mentioned pre-stored standard face images are pre-stored standard face images, and the number can be one or more. Taking two as an example, it can include a first standard face image and a second standard face image. When a user registers with the face recognition system, the face image used when the user registers can be obtained as the initial first standard face image and the initial second standard face image. Of course, the initial second standard face image may not be set.

[0050] S14: Determine whether to update the pre-stored image feature to the first image feature according to the first quality score, the first image feature, and the pre-stored image feature.

[0051] Specifically, the liveness characteristics of the facial image to be identified corresponding to the first quality score can be determined first, and after the liveness characteristics are determined (such as determining that a live face is identified), it is determined whether to update the pre-stored image features to the first image features based on the first quality score, the first image features and the pre-stored image features.

[0052] Therefore, liveness detection is performed on the collected images, so that the standard face image can be updated in real time according to the live face image, thereby improving the security of the face recognition system.

[0053] In one embodiment of the present invention, Figure 2 As shown, the determining whether to update the pre-stored image feature to the first image feature according to the first quality score, the first image feature and the pre-stored image feature may include:

[0054] S21, calculating the similarity between the first image feature and the pre-stored image feature corresponding to the first standard face image to obtain a first similarity.

[0055] S22: Determine whether the first similarity is greater than a first preset similarity threshold.

[0056] It should be noted that, in the embodiment of the present invention, the initial first standard face image is a face image obtained when the user registers, but the non-initial first standard face images are all updated from the face image to be identified. Since the face image to be identified and the initial first standard face image can be acquired by the same acquisition device or by different acquisition devices. Therefore, in order to make a more accurate judgment on the first similarity, it is necessary to select a suitable first preset similarity threshold. Specifically, it can be determined whether the acquisition device of the face image to be identified is the same as that of the first standard face image (for example, whether the type of acquisition device is the same, or the same acquisition device); the first preset similarity threshold is determined according to the judgment result, wherein the first preset similarity threshold determined by the judgment result being the same is greater than the preset similarity threshold determined by the judgment result being different.

[0057] It should be understood that in the case of same-mode comparison, that is, the two facial images used for comparison come from the same type of acquisition device, the similarity obtained by the comparison needs to consider fewer factors and has a smaller error, so the corresponding first preset similarity threshold is relatively large; in the case of different-mode comparison, that is, the two facial images used for comparison come from different types of acquisition devices, such as the facial image to be identified is collected by the above-mentioned acquisition setting, and the first standard facial image is collected by the face registration device, the similarity obtained by the comparison needs to consider more factors and has a larger error, so the corresponding first preset similarity threshold is relatively small.

[0058] S23: If the first similarity is greater than a first preset similarity threshold, face unlocking is performed, and it is determined whether the first quality score is within a second preset quality range, wherein the image quality corresponding to the second preset quality range is better than the image quality corresponding to the first preset quality range.

[0059] Specifically, if the first similarity is greater than the first preset similarity threshold, it means that the current face image to be identified meets the unlocking conditions of the face recognition system, and face unlocking can be performed. Then, the first quality score can be judged again to determine whether the current face image to be identified is suitable for updating the pre-stored standard face image.

[0060] Optionally, if the first similarity is less than or equal to a first preset similarity threshold, the similarity between the first image feature and the pre-stored image feature corresponding to the second standard face image is calculated to obtain a second similarity; it is determined whether the second similarity is greater than a second preset similarity threshold; if the second similarity is greater than the second preset similarity threshold, face unlocking is performed. If the second similarity is less than or equal to the second preset similarity threshold, a first prompt message is issued (such as displaying a preset prompt picture, text message, etc. on the vehicle terminal display screen) to provide a face unlocking failure prompt.

[0061] The second preset quality range may be determined based on the quality of the first standard face image and the quality of the second standard face image. For example, taking the positive correlation between image quality and the first quality score as an example, the second preset quality range may be a range greater than a second quality threshold, the second quality threshold may be the average of the scores of the two standard face images, and the second quality threshold should be greater than or equal to the first quality threshold.

[0062] In this way, face recognition unlocking can be achieved based on at least two standard images, thereby improving the accuracy and probability of face recognition unlocking and enhancing the user experience.

[0063] It should be noted that the updating method of the embodiment of the present invention can be performed during the unlocking process through the face image, that is, the vehicle-mounted face recognition system can execute the updating method of the present invention while performing the conventional face recognition unlocking interaction. The second preset similarity threshold is determined in a similar manner to the first preset similarity threshold, and can be obtained by judging whether the face image to be recognized and the second standard face image are acquired by the same acquisition device.

[0064] S24: If the first quality score is within a second preset quality range, updating the pre-stored image feature corresponding to the first standard face image to the first image feature.

[0065] S25: If the first quality score is not within the second preset quality range, updating the pre-stored image features corresponding to the second standard face image to the first image features.

[0066] It should be noted that when the standard face image is updated according to the face image to be recognized, the above-mentioned face image key point information, image features, etc. also need to be updated in the template library.

[0067] As an example, Figure 3 As shown, it is possible to determine whether the first quality score is within the second preset quality range by comparing the relationship between the first quality score and the second quality threshold. The second quality threshold may be the average of the first standard face image and the second standard face image. If the first quality score is greater than the second quality threshold, the first quality score is within the second preset quality range, and the pre-stored image features corresponding to the first standard face image are updated to the first image features; if the first quality score is less than or equal to the second quality threshold, the first quality score is not within the second preset quality range, and the pre-stored image features corresponding to the second standard face image are updated to the first image features.

[0068] In some embodiments, the quality of the first standard face image obtained by a certain update may be relatively high, resulting in only the second standard face image being updated after multiple consecutive face unlocking successes, while the face image to be identified cannot be face unlocked using the first standard image. Therefore, the number of consecutive times of face unlocking using the second standard face image can also be obtained; if the number of consecutive times reaches the first preset number, the pre-stored image features corresponding to the current first standard face image are updated to the current first image features. In this way, the real-time performance of the standard face images in the template library can be guaranteed.

[0069] It should be noted that after the face unlocking is successful, it is determined whether the consecutive number of times reaches the first preset number. If reached, the standard face image in the template library is directly updated without comparing the relationship between the first quality score and the second preset quality range.

[0070] As an example, when updating the standard face image in the template library, if there is an update, a prompt message can be issued through the vehicle terminal to remind the user that the template library has been updated; if there is no update, no prompt can be issued. Of course, it is also possible to not prompt when there is an update, but prompt when there is no update; to not prompt whether there is an update or not, in which case the update process runs in the background without interaction with the user; or to prompt whether there is an update or not.

[0071] In summary, the face image updating method of the embodiment of the present invention can achieve real-time updating of standard face images, thereby reducing the impact of face variability on the face recognition system and improving the accuracy and usability of face recognition. Moreover, before face recognition, liveness detection is also performed to improve the security of the face recognition system. Face recognition is performed based on at least two standard images, thereby further improving the accuracy of face recognition.

[0072] Furthermore, the present invention also proposes a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned method for updating a facial image is implemented.

[0073] The computer-readable storage medium of an embodiment of the present invention can realize real-time updating of standard facial images when the computer program corresponding to the above-mentioned facial image updating method stored thereon is executed, thereby reducing the impact of facial variability on the facial recognition system and improving the accuracy and ease of use of facial recognition.

[0074] The invention also provides an electronic device.

[0075] In the present invention, the electronic device includes a memory, a processor and a computer program stored in the memory. When the computer program is executed by the processor, the above-mentioned method for updating the facial image is implemented.

[0076] The electronic device of the embodiment of the present invention can realize real-time updating of the standard face image by implementing the above-mentioned face image updating method, thereby reducing the influence of the face variability on the face recognition system and improving the accuracy and usability of face recognition. Moreover, before face recognition, liveness detection is also performed to improve the security of the face recognition system. Face recognition is performed based on at least two standard images, thereby further improving the accuracy of face recognition.

[0077] Figure 4 4 is a structural block diagram of a vehicle according to an embodiment of the present invention.

[0078] like Figure 4 As shown, the vehicle 1000 includes the electronic device 100 described above.

[0079] The vehicle of the embodiment of the present invention can realize real-time updating of the standard face image through the above-mentioned electronic device, thereby reducing the influence of the face variability on the face recognition system and improving the accuracy and usability of face recognition. Moreover, before face recognition, liveness detection is also performed to improve the security of the face recognition system. Face recognition is performed based on at least two standard images, thereby further improving the accuracy of face recognition.

[0080] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0081] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0082] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0083] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “clockwise”, “counterclockwise”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0084] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0085] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0086] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0087] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for updating a face image, characterized in that: The following steps are involved: Collecting facial images to be identified; Evaluating the quality of the face image to be recognized to obtain a first quality score, and determining whether the first quality score is within a first preset quality range; If the first quality score is within the first preset quality range, performing a step of extracting features of the to-be-recognized face image to obtain first image features, and extracting features of the to-be-recognized face image to obtain first image features; Obtaining pre-stored image features of pre-stored standard face images; determining, according to the first quality score, the first image feature, and the pre-stored image feature, whether to update the pre-stored image feature to the first image feature; The pre-stored standard face image includes a first standard face image and a second standard face image, and determining whether to update the pre-stored image feature to the first image feature according to the first quality score, the first image feature, and the pre-stored image feature includes: Calculating the similarity between the first image feature and the pre-stored image feature corresponding to the first standard face image to obtain a first similarity; Determining whether the first similarity is greater than a first preset similarity threshold; If the first similarity is greater than the first preset similarity threshold, face unlocking is performed, and it is determined whether the first quality score is within a second preset quality range, wherein the image quality corresponding to the second preset quality range is better than the image quality corresponding to the first preset quality range; If the first quality score is within the second preset quality range, updating the pre-stored image feature corresponding to the first standard face image to the first image feature; If the first quality score is not within the second preset quality range, the pre-stored image features corresponding to the second standard face image are updated to the first image features.

2. The method for updating a face image as claimed in claim 1, characterized in that: The determining, according to the first quality score, the first image feature and the pre-stored image feature, whether to update the pre-stored image feature to the first image feature further includes: If the first similarity is less than or equal to the first preset similarity threshold, calculating the similarity between the first image feature and the pre-stored image feature corresponding to the second standard face image to obtain a second similarity; Determining whether the second similarity is greater than a second preset similarity threshold; If the second similarity is greater than the second preset similarity threshold, face unlocking is performed.

3. The method for updating a facial image as claimed in claim 2, characterized in that: The updating method further comprises: If the second similarity is less than or equal to the second preset similarity threshold, a first prompt message is issued to prompt that face unlocking has failed.

4. The method for updating a facial image as claimed in claim 2, characterized in that: The updating method further comprises: Obtaining the number of consecutive times of face unlocking using the second standard face image; If the consecutive times reaches a first preset number, the pre-stored image feature corresponding to the current first standard face image is updated to the current first image feature.

5. The method for updating a facial image as claimed in claim 1, characterized in that: The updating method further comprises: The second preset quality range is determined according to the quality of the first standard facial image and the quality of the second standard facial image.

6. The method for updating a facial image as claimed in claim 1, wherein: The updating method further comprises: Determining whether the acquisition device of the face image to be recognized and the first standard face image is the same; Determine the first preset similarity threshold according to the judgment result, The first preset similarity threshold determined when the judgment results are the same is greater than the preset similarity threshold determined when the judgment results are different.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for updating a facial image according to any one of claims 1 to 6 is implemented.

8. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: When the computer program is executed by the processor, the method for updating a facial image according to any one of claims 1 to 6 is implemented.

9. A vehicle, characterized in that: Comprising the electronic device as claimed in claim 8.

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