Fingerprint unlocking method and device based on forged fingerprint feature recognition

Abstract

The present application relates to the field of fingerprint forgery detection and discloses a fingerprint unlocking method and device based on forged fingerprint feature recognition. The method includes: obtaining a fingerprint optical image obtained by an optical fingerprint unlocking component; determining the fingerprint boundary feature of the fingerprint optical image based on the fingerprint optical image using a fingerprint boundary feature recognition model, and determining a first forgery probability corresponding to the fingerprint optical image based on the fingerprint boundary feature; determining the fingerprint blank feature of the fingerprint optical image based on the fingerprint optical image, and determining a second forgery probability corresponding to the fingerprint optical image based on the fingerprint blank feature; when the first forgery probability is greater than or equal to a preset first forgery probability, or when the first forgery probability is less than the preset first forgery probability and when the second forgery probability is greater than or equal to the preset second forgery probability, determining that the fingerprint corresponding to the fingerprint optical image is a forged fingerprint. The present application can improve the electronic device's ability to recognize forged fingerprints.

Description

Technical Field

[0001] The present application relates to the technical field of fingerprint forgery detection, and more specifically, to a fingerprint unlocking method and device based on forged fingerprint feature recognition. Background Art

[0002] Fake fingerprints pose a significant security risk, especially when unlocking electronic devices. With the widespread adoption of devices like smartphones, tablets, and laptops, fingerprint recognition has become a mainstream biometric authentication method. Criminals can obtain a target user's fingerprint information and create a highly realistic fingerprint model based on it, allowing them to easily unlock devices, steal personal data and financial information, or even conduct malicious operations. This can not only lead to privacy breaches and financial losses, but can also trigger more serious social problems, such as identity theft and online fraud.

[0003] Currently, electronic devices still have significant deficiencies in their ability to recognize counterfeit fingerprints. First, the fingerprint recognition systems of many devices are overly reliant on a single biometric feature, such as simple 2D image comparison or low-resolution sensors. This allows counterfeit fingerprints to easily bypass the fingerprint recognition system through surface contact. Second, electronic device manufacturers have insufficiently invested in the security of fingerprint devices, resulting in slow updates to recognition algorithms and an inability to respond to new developments in counterfeiting technology. These factors expose security vulnerabilities in electronic devices' ability to recognize counterfeit fingerprints, further exacerbating the potential societal risks posed by counterfeit fingerprints. Summary of the Invention

[0004] The purpose of this application is to provide a fingerprint unlocking method and device based on forged fingerprint feature recognition, which solves the technical problem of insufficient recognition ability of electronic devices for forged fingerprints and achieves the technical effect of improving the recognition ability of electronic devices for forged fingerprints.

[0005] An embodiment of the present application provides a fingerprint unlocking method based on forged fingerprint feature recognition, the method comprising: obtaining a fingerprint optical image obtained by an optical fingerprint unlocking component; determining a fingerprint boundary feature of the fingerprint optical image based on the fingerprint optical image through a fingerprint boundary feature recognition model, and determining a first forgery probability corresponding to the fingerprint optical image based on the fingerprint boundary feature; determining a fingerprint blank feature of the fingerprint optical image based on the fingerprint optical image through a blank feature recognition model, and determining a second forgery probability corresponding to the fingerprint optical image based on the fingerprint blank feature; when the first forgery probability is greater than or equal to a preset first forgery probability, or when the first forgery probability is less than the preset first forgery probability and when the second forgery probability is greater than or equal to the preset second forgery probability, or when the first forgery probability is greater than or equal to a preset third forgery probability and when the second forgery probability is greater than or equal to a preset fourth forgery probability, determining that the fingerprint corresponding to the fingerprint optical image is a forged fingerprint; wherein the preset third forgery probability is less than the preset first forgery probability, and the preset fourth forgery probability is less than the preset second forgery probability.

[0006] In a possible implementation, the fingerprint boundary features include boundary features corresponding to concave, convex, incomplete, and sharp boundaries of the fingerprint optical image, and the fingerprint blank features include fingerprint blank features corresponding to the number and area of fingerprint blank areas in the fingerprint optical image.

[0007] In another possible implementation, a fingerprint blank feature of the fingerprint optical image is determined based on the fingerprint optical image using a blank feature recognition model, and a second forgery probability corresponding to the fingerprint optical image is determined based on the fingerprint blank feature. The method includes: determining multiple fingerprint blank areas of the fingerprint optical image based on the fingerprint optical image using the blank feature recognition model, adjusting the brightness and contrast of each fingerprint blank area and performing post-filtering processing, binarizing each fingerprint blank area, and identifying fine ridge patterns in each fingerprint blank area using a wavelet transform; determining the fingerprint blank feature based on the number and area of the multiple fingerprint blank areas and the fine ridge pattern in each fingerprint blank area, and determining the second forgery probability corresponding to the fingerprint optical image based on the fingerprint blank feature.

[0008] In another possible implementation, the method further includes: identifying, around each fingerprint blank area of the fingerprint optical image, a fingerprint connection area of adjacent fingerprint lines of the fingerprint optical image, and determining, through a fingerprint connection recognition model, a fingerprint connection feature of the fingerprint optical image based on the fingerprint blank area and the fingerprint connection area around the fingerprint blank area, and determining a fifth forgery probability corresponding to the fingerprint optical image based on the fingerprint connection feature; when the first forgery probability is less than a preset first forgery probability and when the fifth forgery probability is greater than or equal to a preset fifth forgery probability, or when the first forgery probability is greater than or equal to a preset third forgery probability and when the fifth forgery probability is greater than or equal to a preset sixth forgery probability, determining that the fingerprint corresponding to the fingerprint optical image is a forged fingerprint; wherein the preset sixth forgery probability is less than the preset fifth forgery probability.

[0009] In another possible implementation, the method further includes: determining a first number of fingerprint lines connected to each fingerprint connection area, determining a second number of fingerprint lines intersecting each fingerprint blank area, and determining a ratio of the first number to the second number as a fingerprint connection area adjustment factor, and adjusting the fifth forgery probability by multiplying the fingerprint connection area adjustment factor.

[0010] In another possible implementation, the method further includes: determining a completed fingerprint line of the fingerprint in each blank fingerprint area of the fingerprint optical image by a region growing algorithm, and determining that the first forgery probability is greater than or equal to the preset first forgery probability when a misalignment distance between the completed fingerprint line in at least one blank fingerprint area and blank fingerprint lines surrounding the blank fingerprint area is greater than a preset misalignment distance.

[0011] In another possible implementation, the method further includes: determining, based on the fingerprint optical image, the number of fingerprint intersections and the total number of fingerprint lines in the fingerprint optical image using a fingerprint line intersection recognition algorithm, and determining a ratio of the number of fingerprint line intersections to the total number of fingerprint lines as a fingerprint intersection area adjustment factor, and adjusting the fifth forgery probability by multiplying the fifth forgery probability by the fingerprint connection area adjustment factor and the fingerprint intersection area adjustment factor.

[0012] An embodiment of the present application further provides a fingerprint unlocking device based on forged fingerprint feature recognition, comprising a unit for executing any of the methods described above.

[0013] An embodiment of the present application also provides a fingerprint unlocking device based on forged fingerprint feature recognition, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any of the methods described above is implemented.

[0014] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method as described in any one of the above items is implemented.

[0015] An embodiment of the present application further provides a computer program product, including a computer program, which implements the steps of any of the above methods when executed by a processor.

[0016] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0017] The present invention provides a fingerprint unlocking method based on forged fingerprint feature recognition, the method comprising: obtaining a fingerprint optical image obtained by an optical fingerprint unlocking component; determining a fingerprint boundary feature of the fingerprint optical image based on the fingerprint optical image using a fingerprint boundary feature recognition model, and determining a first forgery probability corresponding to the fingerprint optical image based on the fingerprint boundary feature; determining a fingerprint blank feature of the fingerprint optical image based on the fingerprint optical image using a fingerprint blank feature recognition model, and determining a second forgery probability corresponding to the fingerprint optical image based on the fingerprint blank feature; determining the fingerprint corresponding to the fingerprint optical image as a forged fingerprint when the first forgery probability is greater than or equal to a preset first forgery probability, or when the first forgery probability is less than the preset first forgery probability and when the second forgery probability is greater than or equal to a preset second forgery probability, or when the first forgery probability is greater than or equal to a preset third forgery probability and when the second forgery probability is greater than or equal to a preset fourth forgery probability; wherein the preset third forgery probability is less than the preset first forgery probability, and the preset fourth forgery probability is less than the preset second forgery probability. The method in the present invention can evaluate the forgery probability of a fingerprint based on the fingerprint boundary feature and fingerprint blank feature in the fingerprint, thereby ensuring the efficiency and accuracy of forged fingerprint recognition and improving the recognition effect of forged fingerprints. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 A schematic diagram of a flow chart of a fingerprint unlocking method based on forged fingerprint feature recognition provided in an embodiment of the present application;

[0020] Figure 2 Schematic diagram of capturing a forged optical fingerprint image in an embodiment of the present application;

[0021] Figure 3A schematic diagram of an optical fingerprint image provided in an embodiment of the present application;

[0022] Figure 4 This is a schematic diagram of processing a blank area of a fingerprint using the method in an embodiment of the present application;

[0023] Figure 5 A schematic diagram of identifying fingerprint connection areas according to the method in an embodiment of the present application;

[0024] Figure 6 A schematic diagram of identifying the intersection state of fingerprints according to the method in an embodiment of the present application;

[0025] Figure 7 A schematic diagram of the logical structure of a fingerprint unlocking device based on forged fingerprint feature recognition provided by an embodiment of the present application;

[0026] Figure 8 A schematic diagram of the physical structure of a fingerprint unlocking device based on forged fingerprint feature recognition provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0028] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0029] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0030] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0031] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0032] Currently, electronic device manufacturers have not invested enough in the security of fingerprint devices, resulting in slow updates to recognition algorithms and an inability to respond promptly to new developments in counterfeiting technology.

[0033] Based on the above reasons, an embodiment of the present application provides a fingerprint unlocking method based on forged fingerprint feature recognition, the method comprising: obtaining a fingerprint optical image obtained by an optical fingerprint unlocking component; determining a fingerprint boundary feature of the fingerprint optical image based on the fingerprint optical image using a fingerprint boundary feature recognition model, and determining a first forgery probability corresponding to the fingerprint optical image based on the fingerprint boundary feature; determining a fingerprint blank feature of the fingerprint optical image based on the fingerprint optical image using a fingerprint blank feature recognition model, and determining a second forgery probability corresponding to the fingerprint optical image based on the fingerprint blank feature; when the first forgery probability is greater than or equal to a preset first forgery probability, or when the first forgery probability is less than the preset first forgery probability and when the second forgery probability is greater than or equal to the preset second forgery probability, or when the first forgery probability is greater than or equal to a preset third forgery probability and when the second forgery probability is greater than or equal to a preset fourth forgery probability, determining that the fingerprint corresponding to the fingerprint optical image is a forged fingerprint; wherein the preset third forgery probability is less than the preset first forgery probability, and the preset fourth forgery probability is less than the preset second forgery probability. The method in the embodiment of the present application can evaluate the forgery probability of a fingerprint based on the fingerprint boundary feature and fingerprint blank feature in the fingerprint, thereby ensuring the efficiency and accuracy of forged fingerprint recognition and improving the recognition effect of forged fingerprints.

[0034] In some scenarios, a fingerprint unlocking method based on forged fingerprint feature recognition in an embodiment of the present application can be applied to fingerprint unlocking of mobile phones, tablet computers, and laptop computers. It can efficiently identify forged fingerprints during fingerprint unlocking and ensure the accuracy of forged fingerprint recognition.

[0035] The following describes a fingerprint unlocking method based on forged fingerprint feature recognition provided by an embodiment of the present application in detail with reference to specific examples.

[0036] Figure 1 A schematic diagram of a fingerprint unlocking method based on forged fingerprint feature recognition provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes S110 to S130, and S110 to S130 are described in detail below.

[0037] S110: Acquire a fingerprint optical image obtained by the optical fingerprint unlocking component.

[0038] The method in the embodiment of the present application can first collect an optical fingerprint image through the optical fingerprint unlocking component, and identify the optical fingerprint image to determine whether the fingerprint corresponding to the optical fingerprint image is a forged fingerprint.

[0039] Exemplarily, the optical fingerprint unlocking component may be an optical fingerprint unlocking component of an electronic device to which the present method is applied, thereby enabling the electronic device to recognize forged fingerprints through the method in the embodiments of the present application.

[0040] S120: Determine, based on the fingerprint optical image, a fingerprint boundary feature of the fingerprint optical image using a fingerprint boundary feature recognition model, and determine a first forgery probability corresponding to the fingerprint optical image based on the fingerprint boundary feature. Determine, based on the fingerprint optical image, a fingerprint blank feature of the fingerprint optical image using a fingerprint blank feature recognition model, and determine a second forgery probability corresponding to the fingerprint optical image based on the fingerprint blank feature.

[0041] Since the fingerprint boundary features of the fingerprint optical image are relatively obvious, after obtaining the fingerprint optical image, the fingerprint boundary features of the fingerprint optical image can first be determined based on the fingerprint optical image using a fingerprint boundary feature recognition model, and a first forgery probability corresponding to the fingerprint optical image can be determined based on the fingerprint boundary features. The first forgery probability represents the fingerprint forgery probability obtained by boundary detection in the fingerprint optical image.

[0042] Authentic fingerprints, due to the good elasticity of the finger skin, maintain uniform contact with the surface of the object being examined. However, forged fingerprints are prone to blank areas due to the hard material of the forged fingerprint film itself and its limited flexibility and elasticity. Therefore, blank areas in fingerprints can also be used as a basis for identifying forged fingerprints. After acquiring a fingerprint optical image, a blank feature recognition model can be used to further determine the fingerprint blank feature of the fingerprint optical image based on the fingerprint optical image. Based on the fingerprint blank feature, a second forgery probability corresponding to the fingerprint optical image is determined. The second forgery probability represents the fingerprint forgery probability obtained by detecting the blank area in the fingerprint optical image.

[0043] In the embodiment of this application, Figure 2 This is a schematic diagram of the acquisition of a forged optical fingerprint image in an embodiment of the present application, as shown in FIG. Figure 2As shown, a forged fingerprint can be first made, and a forged optical fingerprint image of the forged fingerprint can be collected by an electronic device, and the forged optical fingerprint image can be marked, and then the fingerprint boundary feature recognition model and the blank feature recognition model can be trained by a deep learning model, thereby realizing the training of the fingerprint boundary feature recognition model and the blank feature recognition model.

[0044] Figure 3 A schematic diagram of an optical fingerprint image provided in an embodiment of the present application is shown as follows: Figure 3 As shown, the method in the embodiment of the present application is working, as shown in FIG. Figure 3 As shown in Figure a, the fingerprint boundary feature recognition model can be used to identify Figure 3 The fingerprint boundary features of the fingerprint optical image in Figure a are identified. The fingerprint boundary features can be Figure 3 The fingerprint boundary features corresponding to the image marked in the rectangular box in Figure a.

[0045] like Figure 3 As shown, the method in the embodiment of the present application is working, as shown in FIG. Figure 3 As shown in Figure b, the blank feature recognition model can be used to identify Figure 3 The fingerprint blank feature of the fingerprint optical image in Figure b is identified. The fingerprint blank feature can be Figure 3 The fingerprint blank feature corresponding to the image marked in the rectangular box in Figure b.

[0046] S130: When the first forgery probability is greater than or equal to a preset first forgery probability, or when the first forgery probability is less than the preset first forgery probability and the second forgery probability is greater than or equal to the preset second forgery probability, or when the first forgery probability is greater than or equal to a preset third forgery probability and the second forgery probability is greater than or equal to a preset fourth forgery probability, determine that the fingerprint corresponding to the fingerprint optical image is a forged fingerprint, wherein the preset third forgery probability is less than the preset first forgery probability, and the preset fourth forgery probability is less than the preset second forgery probability.

[0047] After obtaining the first forgery probability and the second forgery probability, when the first forgery probability is greater than or equal to the preset first forgery probability, it means that the first forgery probability obtained based on the fingerprint boundary feature is larger, and then the fingerprint corresponding to the fingerprint optical image can be judged as a forged fingerprint based solely on the first forgery probability.

[0048] After obtaining the first forgery probability and the second forgery probability, when the first forgery probability is less than the preset first forgery probability and when the second forgery probability is greater than or equal to the preset second forgery probability, it means that the first forgery probability obtained based on the fingerprint boundary feature is smaller and the second forgery probability obtained based on the fingerprint blank feature is larger, and thus the fingerprint corresponding to the fingerprint optical image can be determined as a forged fingerprint based solely on the second forgery probability.

[0049] After obtaining the first forgery probability and the second forgery probability, when the first forgery probability is greater than or equal to the preset third forgery probability, and when the second forgery probability is greater than or equal to the preset fourth forgery probability, the preset third forgery probability is less than the preset first forgery probability, and the preset fourth forgery probability is less than the preset second forgery probability, it means that the first forgery probability obtained based on the fingerprint boundary feature and the second forgery probability obtained based on the fingerprint blank feature are both within a relatively large numerical range, and neither can be judged as a forged fingerprint based on the first forgery probability and the second forgery probability alone. The fingerprint corresponding to the fingerprint optical image can be judged as a forged fingerprint based on the first forgery probability and the second forgery probability in combination.

[0050] The beneficial effect of the above implementation method is that the fingerprint boundary features of the fingerprint optical image are relatively obvious. When the first forgery probability obtained from the fingerprint boundary features is relatively high, the fingerprint corresponding to the fingerprint optical image can be judged as a forged fingerprint based solely on the first forgery probability, which can reduce the computational complexity of forged fingerprint recognition and ensure the accuracy and efficiency of forged fingerprint recognition.

[0051] The beneficial effect of the above implementation method is that the first forgery probability obtained based on the fingerprint boundary feature is smaller, and the second forgery probability obtained based on the fingerprint blank feature is larger. Therefore, the fingerprint corresponding to the fingerprint optical image can be judged as a forged fingerprint based solely on the second forgery probability, which can ensure the recognition accuracy of forged fingerprints.

[0052] The beneficial effect of the above implementation method is that when it is impossible to judge that a fingerprint is a forged fingerprint based on the first forgery probability and the second forgery probability alone, the fingerprint corresponding to the fingerprint optical image can be judged as a forged fingerprint based on the first forgery probability and the second forgery probability, thereby further ensuring the recognition accuracy of forged fingerprints.

[0053] In some implementations, the fingerprint boundary features include boundary features corresponding to concave, convex, incomplete, and sharp boundaries of the fingerprint optical image, and the fingerprint blank features include fingerprint blank features corresponding to the number and area of fingerprint blank areas in the fingerprint optical image.

[0054] like Figure 3 As shown in the area selected by Figure a in the figure, in the production of forged fingerprints, "artificial traces" often appear near the fingerprint boundary. These "artificial traces" are defects caused by operations such as cutting and trimming during the formation of the fingerprint film. Fingerprint boundary features can include boundary features corresponding to the concave, convex, incomplete, and sharp boundaries of the fingerprint optical image. Therefore, forged fingerprints can be identified based on the concave, convex, incomplete, and sharp boundaries of the fingerprint optical image.

[0055] like Figure 3As shown in the area selected in Figure b, since the material of the forged fingerprint film itself is relatively hard and the flexibility and elasticity of the forged fingerprint film are limited, the fingerprint blank feature includes the fingerprint blank feature corresponding to the number and area of the fingerprint blank areas in the fingerprint optical image, and then the forged fingerprint can be identified based on the number and area of the fingerprint blank areas in the fingerprint optical image.

[0056] The beneficial effect of the above implementation is that it can identify forged fingerprints by combining the concave-convex, incomplete and sharp boundaries of the fingerprint optical image, thereby improving the recognition accuracy of forged fingerprints.

[0057] The above implementation method has the beneficial effect of being able to identify forged fingerprints based on the number and area of fingerprint blank areas in the fingerprint optical image.

[0058] In some implementations, in the above S120, a fingerprint blank feature of the fingerprint optical image is determined based on the fingerprint optical image using a blank feature recognition model, and a second forgery probability corresponding to the fingerprint optical image is determined based on the fingerprint blank feature, including S121 to S122. S121 to S122 are described in detail below.

[0059] S121. Using a blank feature recognition model, determine multiple fingerprint blank areas in the fingerprint optical image based on the fingerprint optical image, adjust the brightness and contrast of each fingerprint blank area, perform post-filtering processing, binarize each fingerprint blank area, and identify fine line patterns in each fingerprint blank area using wavelet transform.

[0060] Figure 4 This is a schematic diagram of processing the blank area of a fingerprint using the method in an embodiment of the present application. Figure 4 As shown, when the method in the embodiment of the present application is working, a blank feature recognition model can be used to determine multiple fingerprint blank areas of the fingerprint optical image according to the fingerprint optical image, adjust the brightness and contrast of each fingerprint blank area, perform post-filtering processing, and binarize each fingerprint blank area. The fine line pattern in each fingerprint blank area is identified through wavelet transform to identify the fingerprint lines in the fingerprint blank area.

[0061] For example, Figure 4 As shown in Figure a, for Figure 4 The fingerprint blank area in the rectangular frame in Figure a can be enhanced by the above method to achieve the recognition accuracy of the fingerprint line in the fingerprint blank area.

[0062] For example, Figure 4As shown in Figure b, by performing image enhancement using the above method, the fingerprint lines in the blank area of the fingerprint can be identified, and then whether the blank area is a real fingerprint can be judged based on the fingerprint lines in the blank area of the fingerprint.

[0063] S122: Determine a fingerprint blank feature based on the number and area of the plurality of fingerprint blank areas and the fine line pattern in each fingerprint blank area, and determine a second forgery probability corresponding to the fingerprint optical image based on the fingerprint blank feature.

[0064] Since the blank area of a real fingerprint is often caused by insufficient pressure on the fingerprint, there are often fine fingerprint lines in the blank area of the real fingerprint; while forged fingerprints are often caused by defects in the forged fingerprint production process, so there are often no fingerprint lines in the blank area of the forged fingerprint.

[0065] Based on the above reasons, when performing forged fingerprint identification, the fingerprint blank feature can be determined based on the number and area of multiple fingerprint blank areas and the fine line pattern in each fingerprint blank area, and the second forgery probability corresponding to the fingerprint optical image can be determined based on the fingerprint blank feature. This makes it possible to judge the forged fingerprint in combination with the fine line pattern in each fingerprint blank area, thereby improving the recognition probability of forged fingerprints.

[0066] The beneficial effect of the above implementation is that the forged fingerprint is judged in combination with the fine line pattern in the blank area of each fingerprint, thereby improving the recognition probability of the forged fingerprint.

[0067] The beneficial effect of the above implementation method is that the fingerprint blank features are determined based on the number and area of multiple fingerprint blank areas and the fine line patterns in each fingerprint blank area, which can identify forged fingerprints by combining the fingerprint as a whole with the fine lines in each fingerprint blank area, thereby improving the accuracy of forged fingerprint identification.

[0068] In some implementations, the above method further includes S210 to S220, and S210 to S220 are described in detail below.

[0069] S210. Identify fingerprint connection areas of adjacent fingerprint lines of the fingerprint optical image around each fingerprint blank area in the fingerprint optical image, and determine a fingerprint connection feature of the fingerprint optical image based on the fingerprint blank area and the fingerprint connection areas around the fingerprint blank area using a fingerprint connection recognition model, and determine a fifth forgery probability corresponding to the fingerprint optical image based on the fingerprint connection feature.

[0070] Figure 5 This is a schematic diagram of identifying fingerprint connection areas using the method in an embodiment of the present application, as shown in FIG. Figure 5As shown, since a connected area is often generated around a blank defect during the production of a forged fingerprint, a fingerprint connected area of adjacent fingerprint lines of the fingerprint optical image can be identified around each blank area of the fingerprint optical image during forged fingerprint identification. Adjacent fingerprint lines around each blank area of the fingerprint often generate a connected area due to the pressing during the production of the forged fingerprint, so the forged fingerprint can be identified through the adjacent fingerprint lines around each blank area of the fingerprint.

[0071] After determining the adjacent fingerprint lines around each fingerprint blank area, the fingerprint connection feature of the fingerprint optical image can be determined based on the fingerprint blank area and the fingerprint connection area around the fingerprint blank area through the fingerprint connection recognition model, and the fifth forgery probability corresponding to the fingerprint optical image can be determined based on the fingerprint connection feature. The fifth forgery probability represents the probability of judging that the fingerprint is a forged fingerprint through the connection area around the fingerprint blank area.

[0072] Exemplarily, when determining the fingerprint connection feature of the fingerprint optical image based on the fingerprint blank area and the fingerprint connection area around the fingerprint blank area, the fingerprint connection feature of the fingerprint optical image can be determined based on all the fingerprint blank areas and the fingerprint connection area around each fingerprint blank area.

[0073] S220: When the first forgery probability is less than a preset first forgery probability and the fifth forgery probability is greater than or equal to the preset fifth forgery probability, or when the first forgery probability is greater than or equal to the preset third forgery probability and the fifth forgery probability is greater than or equal to the preset sixth forgery probability, determine that the fingerprint corresponding to the fingerprint optical image is a forged fingerprint, wherein the preset sixth forgery probability is less than the preset fifth forgery probability.

[0074] When performing forged fingerprint identification, when the first forgery probability is less than the preset first forgery probability, and when the fifth forgery probability is greater than or equal to the preset fifth forgery probability, it means that the probability of judging the fingerprint as a forged fingerprint through the fingerprint boundary features of the fingerprint optical image is low, while the probability of judging the fingerprint as a forged fingerprint through the connected area around the blank area of the fingerprint is high. Therefore, the fingerprint corresponding to the fingerprint optical image can be judged as a forged fingerprint, thereby achieving accurate identification of forged fingerprints.

[0075] When performing forged fingerprint identification, when the first forgery probability is greater than or equal to the preset third forgery probability, and when the fifth forgery probability is greater than or equal to the preset sixth forgery probability, the preset sixth forgery probability is less than the preset fifth forgery probability, indicating that the first forgery probability obtained based on the fingerprint boundary feature and the fifth forgery probability obtained based on the fingerprint blank feature are both within a relatively large numerical range, and neither can be judged as a forged fingerprint based on the first forgery probability and the fifth forgery probability alone. The fingerprint corresponding to the fingerprint optical image can be judged as a forged fingerprint based on the first forgery probability and the fifth forgery probability in combination, which can ensure the recognition accuracy of forged fingerprints and avoid missed detections.

[0076] The beneficial effect of the above implementation method is that when the probability of judging a fingerprint as a forged fingerprint by the fingerprint boundary features of the fingerprint optical image is low, and when the probability of judging a fingerprint as a forged fingerprint by the connected area around the blank area of the fingerprint is high, the fingerprint corresponding to the fingerprint optical image can be judged as a forged fingerprint, thereby ensuring the recognition accuracy of forged fingerprints.

[0077] The beneficial effect of the above implementation method is that when it is impossible to judge that a fingerprint is a forged fingerprint based on the first forgery probability and the fifth forgery probability alone, the fingerprint corresponding to the fingerprint optical image can be judged as a forged fingerprint based on the first forgery probability and the fifth forgery probability in combination, thereby ensuring the accuracy of identifying forged fingerprints.

[0078] In some implementations, the method further includes: determining a first number of fingerprint lines connected to each fingerprint connection area, determining a second number of fingerprint lines intersecting each fingerprint blank area, determining a ratio of the first number to the second number as a fingerprint connection area adjustment factor, and adjusting the fifth forgery probability by multiplying the fingerprint connection area adjustment factor.

[0079] When forging fingerprints, the fingerprint lines around the forged fingerprints are often connected due to the production process. However, clean and real fingerprints often do not have fingerprint lines connected around the blank areas. Therefore, when performing forged fingerprint recognition, Figure 5 As shown, a first number of fingerprint lines connected to each fingerprint connection area can be determined. The first number represents the number of fingerprint lines connected by each fingerprint connection area. The larger the first number, the higher the possibility that the fingerprint is a forged fingerprint.

[0080] When performing forged fingerprint recognition, Figure 5 As shown, the second number of fingerprint lines intersecting each fingerprint blank area can be determined. The second number represents the number of fingerprint lines spanned by the fingerprint blank area. The larger the second number, the more fingerprint lines are affected by the fingerprint blank area.

[0081] After obtaining the first number and the second number, a ratio of the first number to the second number can be determined as a fingerprint connection area adjustment factor. The fingerprint connection area adjustment factor represents the ratio of the connection between fingerprint lines. The fingerprint connection area adjustment factor can then be used to evaluate the probability of whether the fingerprint is a forged fingerprint.

[0082] After obtaining the fingerprint connection area adjustment factor, the fifth forgery probability can be adjusted by multiplying the fingerprint connection area adjustment factor to achieve the adjustment of the fifth forgery probability, and then forged fingerprint detection can be performed based on the adjusted fifth fingerprint forgery probability.

[0083] For example, when determining the first number of fingerprint lines connected to each fingerprint connection area, an edge detection algorithm (such as Canny edge detection) can be used on the binary image to extract the contours of the fingerprint lines, and the places where the fingerprint lines in each fingerprint connection area intersect or bifurcate can be identified, thereby realizing the identification of the first number of fingerprint lines connected to each fingerprint connection area.

[0084] The beneficial effect of the above implementation method is that due to the manufacturing process, the fingerprint lines around the forged fingerprint may be connected, and the probability of fingerprint forgery can be evaluated based on the connection status of the fingerprint lines connected to each fingerprint connection area, thereby improving the recognition accuracy of forged fingerprints.

[0085] The beneficial effect of the above implementation method is also to determine the first number of fingerprint lines connected to each fingerprint connection area, and determine the second number of fingerprint lines intersecting with each fingerprint blank area, and determine the ratio of the first number to the second number as the fingerprint connection area adjustment factor, and the fifth forgery probability can be adjusted according to the fingerprint connection area adjustment factor, thereby further improving the accuracy of forged fingerprint recognition.

[0086] In some implementations, the above method further includes: determining a completed fingerprint line of the fingerprint in each fingerprint blank area of the fingerprint optical image by a region growing algorithm, and determining that the first forgery probability is greater than or equal to the preset first forgery probability when a misalignment distance between the completed fingerprint line in at least one fingerprint blank area and blank fingerprint lines surrounding the fingerprint blank area is greater than a preset misalignment distance.

[0087] When forged fingerprints are produced, the fingerprint lines of the forged fingerprints are often misaligned on both sides of the blank area of the fingerprint due to reasons in the production process. Then, the forged fingerprints can be identified by identifying the features of the fingerprint lines that are misaligned on both sides of the blank area of the fingerprint.

[0088] When identifying the features of a forged fingerprint in which the fingerprint line is misaligned on both sides of the blank area of the fingerprint, the region growing algorithm can be used to determine the completed fingerprint line of the fingerprint in each blank area of the fingerprint optical image. The completed fingerprint line is based on the estimated fingerprint line position.

[0089] For example, when determining the completed fingerprint line of the fingerprint in each fingerprint blank area of the fingerprint optical image, the fingerprint line can be completed from one side of each fingerprint blank area to the fingerprint blank area using a region growing algorithm to obtain the completed fingerprint line in the fingerprint blank area.

[0090] After obtaining the completed fingerprint lines in the fingerprint blank area, the corresponding relationships of the fingerprint lines can be determined one by one from one end to the other end of the fingerprint blank area according to the corresponding relationships of the fingerprint lines.

[0091] After obtaining the completed fingerprint line in the blank area of the fingerprint, when the misalignment distance between at least one completed fingerprint line in the blank area of the fingerprint and the blank fingerprint lines surrounding the blank area of the fingerprint is greater than the preset misalignment distance, it indicates that the misalignment amplitude between the completed fingerprint line and the fingerprint lines surrounding the blank area of the fingerprint is too large, and thus it can be determined that the first forgery probability is greater than or equal to the preset first forgery probability. Furthermore, it can be determined that the fingerprint corresponding to the fingerprint optical image is a forged fingerprint based on the first forged fingerprint probability.

[0092] For example, the preset misalignment distance may be 1.5 to 2 times the distance between adjacent fingerprint lines closest to the fingerprint line where the misalignment occurs.

[0093] The beneficial effect of the above implementation is that the recognition accuracy of the forged fingerprint can be further improved by recognizing the features of the forged fingerprint where the fingerprint lines are misaligned on both sides of the blank area of the fingerprint.

[0094] The beneficial effect of the above implementation method is that, through the region growing algorithm, the fingerprint's completed fingerprint line is determined in each fingerprint blank area of the fingerprint optical image, and the forged fingerprint is judged based on the completed fingerprint line, thereby ensuring the recognition efficiency and accuracy of forged fingerprints.

[0095] In some implementations, the method further includes: determining, based on the fingerprint optical image, the number of fingerprint intersections and the total number of fingerprint lines in the fingerprint optical image using a fingerprint line intersection recognition algorithm, and determining a ratio of the number of fingerprint line intersections to the total number of fingerprint lines as a fingerprint intersection area adjustment factor, and adjusting the fifth forgery probability by multiplying the fifth forgery probability by the fingerprint connection area adjustment factor and the fingerprint intersection area adjustment factor.

[0096] Forged fingerprints often overlap due to the manufacturing process, so forged fingerprints can be identified based on the features of the overlap.

[0097] Figure 6 This is a schematic diagram of the method for identifying the intersection state of fingerprints in an embodiment of the present application, as shown in FIG. Figure 6 As shown in the rectangular area in , when identifying a forged fingerprint based on the fingerprint intersection feature, the fingerprint line intersection recognition algorithm can be used to determine the number of fingerprint intersections and the total number of fingerprint lines in the fingerprint optical image based on the fingerprint optical image. The number of fingerprint intersections and the total number of fingerprint lines represent the state of intersection of the fingerprint lines.

[0098] After obtaining the number of fingerprint intersections and the total number of fingerprint lines, the ratio of the number of fingerprint line intersections to the total number of fingerprint lines can be determined as the fingerprint intersection area adjustment factor. The fingerprint intersection area adjustment factor represents the proportion of fingerprint lines that produce fingerprint intersections in all fingerprints.

[0099] After obtaining the fingerprint connection area adjustment factor, the fifth forgery probability can be adjusted by multiplying the fingerprint connection area adjustment factor and the fingerprint intersection area adjustment factor on the fifth forgery probability, thereby adjusting the fifth forgery probability of forged fingerprint identification according to the characteristics of the fingerprint connection area and the characteristics of the fingerprint intersection area, further improving the accuracy of forged fingerprint identification.

[0100] For example, the fingerprint line intersection recognition algorithm can use the edge detection algorithm to extract the contour of the fingerprint line and refine the extracted edge to make the fingerprint line thinner and continuous to facilitate subsequent intersection detection. After obtaining the fingerprint line, the connected component analysis can be used to identify the area where the fingerprint lines intersect, and the identified intersection areas can be counted to obtain the number of fingerprint line intersections.

[0101] For example, the total number of fingerprint lines can be counted by analyzing the number of pixels or length of the fingerprint lines. Specifically, the fingerprint lines can be detected using Hough Transform.

[0102] The beneficial effect of the above implementation is that when forged fingerprints are crossed due to the manufacturing process, high-precision recognition of the forged fingerprints can be achieved by identifying the crossing features between the fingerprints.

[0103] The beneficial effect of the above implementation method is that the ratio of the number of fingerprint line intersections to the total number of fingerprint lines can be determined as a fingerprint intersection area adjustment factor, and the fingerprint intersection area adjustment factor can be used to judge forged fingerprints, thereby ensuring the recognition accuracy of forged fingerprints.

[0104] The beneficial effect of the above implementation method is that after obtaining the fingerprint intersection area adjustment factor, the fifth forgery probability is multiplied by the fingerprint connection area adjustment factor and the fingerprint intersection area adjustment factor to adjust the fifth forgery probability. Combined with the above fingerprint connection area adjustment factor and fingerprint intersection area adjustment factor, a comprehensive judgment of the forged fingerprint is made, which ensures the recognition accuracy of the forged fingerprint and can avoid false detection.

[0105] An embodiment of the present application further provides a fingerprint unlocking device based on forged fingerprint feature recognition, comprising a unit for executing any of the methods described above.

[0106] Figure 7 A schematic diagram of the logical structure of a fingerprint unlocking device based on forged fingerprint feature recognition provided by an embodiment of the present application is shown as follows: Figure 7 As shown, the device 1 of this embodiment includes a processing unit 11, a storage unit 12, and a transceiver unit 13. The processing unit 11 is used to process data, the storage unit 12 is used to store data, and the transceiver unit 13 is used to send and receive data. The processing unit 11, the storage unit 12, and the transceiver unit 13 cooperate with each other to implement the above method. The beneficial effects of the embodiment of the present application have been described in the above method and will not be repeated here.

[0107] An embodiment of the present application also provides a fingerprint unlocking device based on forged fingerprint feature recognition, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any of the methods described above is implemented.

[0108] Figure 8 A schematic diagram of the physical structure of a fingerprint unlocking device based on forged fingerprint feature recognition provided by an embodiment of the present application is shown as follows: Figure 8 As shown, the apparatus 2 of this embodiment includes: at least one processor 20 ( Figure 8 Only one processor 20 is shown in the figure), a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor 20. When the processor 20 executes the computer program 22, the steps of any of the above-mentioned method embodiments are implemented. The beneficial effects of the embodiments of the present application have been described in the above-mentioned methods and will not be repeated here.

[0109] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0110] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0111] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0112] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned various method embodiments when executing the computer program product.

[0113] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process of the above-mentioned method embodiment by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can at least include: any entity or device capable of carrying computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, mobile hard drive, magnetic disk, or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0114] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0115] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0116] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0117] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0118] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A fingerprint unlocking method based on forged fingerprint feature recognition, characterized in that: The method comprises: Acquire a fingerprint optical image obtained by the optical fingerprint unlocking component; Determining, based on the fingerprint optical image, a fingerprint boundary feature of the fingerprint optical image using a fingerprint boundary feature recognition model, and determining a first forgery probability corresponding to the fingerprint optical image based on the fingerprint boundary feature; determining, based on the fingerprint optical image, a fingerprint blank feature of the fingerprint optical image using a fingerprint blank feature recognition model, and determining a second forgery probability corresponding to the fingerprint optical image based on the fingerprint blank feature; When the first forgery probability is greater than or equal to a preset first forgery probability, or when the first forgery probability is less than the preset first forgery probability and when the second forgery probability is greater than or equal to the preset second forgery probability, or when the first forgery probability is greater than or equal to a preset third forgery probability and when the second forgery probability is greater than or equal to a preset fourth forgery probability, the fingerprint corresponding to the fingerprint optical image is determined to be a forged fingerprint; wherein the preset third forgery probability is less than the preset first forgery probability, and the preset fourth forgery probability is less than the preset second forgery probability; The method further comprises: Identifying fingerprint connection areas of adjacent fingerprint lines of the fingerprint optical image around each fingerprint blank area of the fingerprint optical image, and determining a fingerprint connection feature of the fingerprint optical image based on the fingerprint blank area and the fingerprint connection areas around the fingerprint blank area using a fingerprint connection recognition model, and determining a fifth forgery probability corresponding to the fingerprint optical image based on the fingerprint connection feature; When the first forgery probability is less than the preset first forgery probability and when the fifth forgery probability is greater than or equal to the preset fifth forgery probability, or when the first forgery probability is greater than or equal to the preset third forgery probability and when the fifth forgery probability is greater than or equal to the preset sixth forgery probability, the fingerprint corresponding to the fingerprint optical image is determined to be a forged fingerprint; wherein the preset sixth forgery probability is less than the preset fifth forgery probability.

2. The method according to claim 1, wherein Fingerprint boundary features include boundary features corresponding to concave, convex, incomplete, and sharp boundaries of the fingerprint optical image, and fingerprint blank features include fingerprint blank features corresponding to the number and area of fingerprint blank areas in the fingerprint optical image.

3. The method according to claim 2, wherein Determining a fingerprint blank feature of the fingerprint optical image based on the fingerprint optical image using a blank feature recognition model, and determining a second forgery probability corresponding to the fingerprint optical image based on the fingerprint blank feature, including: Using a blank feature recognition model, multiple fingerprint blank areas are determined based on the fingerprint optical image. The brightness and contrast of each fingerprint blank area are adjusted and then filtered. Each fingerprint blank area is binarized and the fine line patterns in each fingerprint blank area are identified through wavelet transform. A fingerprint blank feature is determined according to the number and area of the plurality of fingerprint blank areas and the fine line pattern in each fingerprint blank area, and a second forgery probability corresponding to the fingerprint optical image is determined according to the fingerprint blank feature.

4. The method according to claim 1, wherein The method further comprises: Determine a first number of fingerprint lines connected to each fingerprint connection area, determine a second number of fingerprint lines intersecting each fingerprint blank area, and determine a ratio of the first number to the second number as a fingerprint connection area adjustment factor, and multiply the fifth forgery probability by the fingerprint connection area adjustment factor to adjust the fifth forgery probability.

5. The method according to claim 4, wherein The method further comprises: A region growing algorithm is used to determine the completed fingerprint line of the fingerprint within each blank fingerprint area of the fingerprint optical image. When the misalignment distance between the completed fingerprint line within at least one blank fingerprint area and the blank fingerprint lines surrounding the blank fingerprint area is greater than a preset misalignment distance, it is determined that the first forgery probability is greater than or equal to the preset first forgery probability.

6. A fingerprint unlocking device based on forged fingerprint feature recognition, characterized in that: Comprising means for performing the method according to any one of claims 1 to 5.

7. A fingerprint unlocking device based on forged fingerprint feature recognition, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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