CN115019364A - Identity authentication method and device based on face recognition, electronic equipment and medium - Google Patents
Identity authentication method and device based on face recognition, electronic equipment and medium Download PDFInfo
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- CN115019364A CN115019364A CN202210547724.2A CN202210547724A CN115019364A CN 115019364 A CN115019364 A CN 115019364A CN 202210547724 A CN202210547724 A CN 202210547724A CN 115019364 A CN115019364 A CN 115019364A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/36—User authentication by graphic or iconic representation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
Abstract
The embodiment of the invention discloses an identity authentication method, an identity authentication device, electronic equipment and a medium based on face recognition, and belongs to the field of image processing. The method comprises the following steps: acquiring a face recognition image, carrying out image analysis on the face recognition image, and judging whether a face exists or not; if the human face exists, obtaining human face feature point data; calculating a quality factor according to the face feature point data, and obtaining a face quality score according to the quality factor and a corresponding influence value; if the face quality score is larger than a first threshold value, performing identity authentication according to the face recognition image; wherein the quality factor comprises at least one of: the human face fuzzy degree, the human face accuracy, the upper half face shielding rate, the lower half face shielding rate, the illumination recognition influence degree, the image noise influence value and the posture influence value. The embodiment of the invention can improve the identity authentication efficiency and accuracy.
Description
Technical Field
The present invention relates to the field of image processing, and in particular, to an identity authentication method and apparatus based on face recognition, an electronic device, and a medium.
Background
Face recognition is a technique of performing identification based on facial feature information of a person. Currently, face recognition technology is widely applied in the field of privacy or security related authentication, such as face recognition access attendance, face recognition security doors, face unlocking, face recognition payment, and the like.
The recognition rate of the face recognition by using the existing face recognition method is low, and the on-site recognition rate is low due to the fact that the light environment is different when the face is shot or the problems that an individual user opens and looks and repairs pictures, the pictures are too large, the picture quality is poor, the picture resolution is low and the like exist possibly in the input face information; the face recognition rate may also be low because the face is blocked during the input.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides an identity authentication method, an identity authentication device, electronic equipment and a medium based on face recognition, which can improve the identity authentication efficiency and accuracy.
The embodiment of the invention provides an identity authentication method based on face recognition on the one hand, which comprises the following steps: acquiring a face recognition image, carrying out image analysis on the face recognition image, and judging whether a face exists or not; if the human face exists, obtaining human face feature point data; calculating a quality factor according to the face feature point data, and obtaining a face quality score according to the quality factor and a corresponding influence value; if the face quality score is larger than a first threshold value, performing identity authentication according to the face recognition image; wherein the quality factor comprises at least one of: the human face fuzzy degree, the human face accuracy, the upper half face shielding rate, the lower half face shielding rate, the illumination recognition influence degree, the image noise influence value and the posture influence value.
According to some embodiments of the present invention, the obtaining a face recognition image, performing image analysis on the face image, and determining whether a face exists includes: acquiring a face recognition image and storing the face recognition image through an xy-axis data matrix corresponding to an image pixel dot matrix to obtain face recognition data; and carrying out feature matching on the numerical distribution of the face identification data, and judging whether a face exists or not according to the feature value.
According to some embodiments of the invention, the obtaining the face feature point data comprises: acquiring the number and position coordinates of face characteristic points corresponding to each face; the face feature points at least comprise: eyebrows, eyes, nose, mouth, and outer contour.
According to some embodiments of the invention, said calculating a quality factor from said face feature point data comprises one or more of: calculating the face ambiguity according to the number of the face characteristic points and the face direction; the face credibility is obtained by detecting the number of face feature points of five sense organs; calculating the number of the face characteristic points of the upper half face to obtain the upper half face shielding rate; obtaining illumination intensity by comprehensively taking values of an RGB channel and an HSV channel of the face recognition image, and obtaining illumination recognition influence degree by combining the number of the face characteristic points; calculating the number of the face characteristic points of the lower half face to obtain the shielding rate of the lower half face; acquiring first face characteristic point data identified by the face recognition image and second face characteristic point data identified by the de-noised face recognition image, and comparing the first face characteristic point data with the second face characteristic point data to obtain an image noise influence value; and carrying out gesture recognition on the face recognition image to obtain a gesture influence score.
According to some embodiments of the invention, if the face quality score is less than or equal to the first threshold, the step of obtaining the face recognition image is returned.
According to some embodiments of the invention, the identity authentication according to the face recognition image comprises: based on a face database, carrying out face matching on the face recognition image according to the face feature point data; and if the matching is successful, determining that the identity authentication is passed, and outputting the face credibility.
According to some embodiments of the invention, the outputting the face credibility comprises: and obtaining the face credibility according to the face quality scores and outputting the face credibility.
In another aspect, an embodiment of the present invention provides an identity authentication apparatus based on face recognition, including: the system comprises a first module, a second module and a third module, wherein the first module is used for acquiring a face recognition image, carrying out image analysis on the face recognition image and judging whether a face exists or not; the second module is used for acquiring face feature point data when the first module determines that the face identification image has a face; the third module is used for calculating a quality factor according to the face feature point data and obtaining a face quality score according to the quality factor and a corresponding influence value; the fourth module is used for carrying out identity authentication according to the face recognition image when the face quality score is larger than a first threshold value; wherein the quality factor comprises at least one of: the human face fuzzy degree, the human face accuracy, the upper half face shielding rate, the lower half face shielding rate, the illumination recognition influence degree, the image noise influence value and the posture influence value.
In another aspect, an embodiment of the present invention provides an electronic device, including a processor and a memory; the memory is used for storing programs; the processor executes the program to implement the method described above.
Another aspect of the embodiments of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method described above.
The embodiment of the invention has at least the following beneficial effects: the method of the embodiment of the invention obtains the face characteristic points and analyzes the data of the face characteristic points to obtain whether the current face recognition image is a low-quality image, and if the current face recognition image is the low-quality image, the face image is obtained again for identity verification, so that the accuracy of face recognition is improved and the identity authentication efficiency is improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method according to another embodiment of the present invention;
FIG. 3 is a schematic flow chart of a method according to another embodiment of the present invention;
FIG. 4 is a block schematic diagram of an apparatus of an embodiment of the invention;
fig. 5 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and more than, less than, more than, etc. are understood as excluding the present number, and more than, less than, etc. are understood as including the present number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
Referring to fig. 1, an embodiment of the present invention provides an identity authentication method based on face recognition, including the following steps:
s100, acquiring a face recognition image, and carrying out image analysis on the face recognition image; the embodiment may be to acquire the face recognition image by taking a picture with a camera of the face recognition device or selecting a picture from a memory.
S200, judging whether a face exists or not, and if the face exists, executing the step S300; in this embodiment, the face recognition image is analyzed to determine whether a face exists, and a specific analysis method may be to store the face recognition image by an x/y-axis digital matrix corresponding to an image pixel lattice, perform feature matching on the numerical distribution, and determine whether a face exists by a feature value.
S300, acquiring face feature point data; the facial feature point data of this embodiment may be a plurality of preset facial feature points, including several points of eyebrows, several points of eyes, several points of noses, several points of mouths, and several points of outlines.
S400, calculating a quality factor according to the data of the face feature points, and obtaining a face quality score according to the quality factor and a corresponding influence value; it is to be understood that the quality factor includes at least one of: the human face fuzzy degree, the human face accuracy, the upper half face shielding rate, the lower half face shielding rate, the illumination recognition influence degree, the image noise influence value and the posture influence value.
S500, judging whether the face quality score is larger than a first threshold value; if the face quality score is greater than the first threshold, executing step S600; in this embodiment, the higher the face quality score is, the higher the accuracy of face matching is, and this step ensures the quality of the face recognition image currently used for identity authentication, so as to improve the recognition rate.
And S600, performing identity authentication according to the face recognition image.
It should be noted that, in some embodiments, the device for performing face recognition identity authentication may be a terminal having a sensor and a camera, including but not limited to a smart phone, a tablet computer, a PC computer, etc. with a camera, or may be an access control system that needs identity authentication, and the face image information can be collected by arranging a camera above or at a side of the access control system, where the camera is convenient to collect face information.
In some embodiments, acquiring a face recognition image, performing image analysis on the face image, and determining whether a face exists includes: acquiring a face recognition image and storing the face recognition image through an xy-axis data matrix corresponding to an image pixel dot matrix to obtain face recognition data; and carrying out feature matching on the numerical distribution of the face identification data, and judging whether the face exists or not according to the feature value.
In some embodiments, the method of obtaining face feature point data includes: acquiring the number and position coordinates of face characteristic points corresponding to each face; wherein, the characteristic points of the human face at least comprise: eyebrows, eyes, nose, mouth and outer contour.
In some embodiments, the step of calculating a quality factor from the face feature point data comprises one or more of: calculating the face ambiguity according to the number of the face characteristic points and the face direction; the face credibility is obtained by detecting the number of face feature points of five sense organs; calculating the number of the face characteristic points of the upper half face to obtain the upper half face shielding rate; obtaining illumination intensity by comprehensively taking values of an RGB channel and an HSV channel of a face recognition image, and obtaining illumination recognition influence degree by combining the number of face characteristic points; calculating the number of the face feature points of the lower half face to obtain the shielding rate of the lower half face; acquiring first face characteristic point data identified by a face identification image and second face characteristic point data identified by a de-noised face identification image, and comparing the first face characteristic point data with the second face characteristic point data to obtain an image noise influence value; and carrying out gesture recognition on the face recognition image to obtain a gesture influence score.
In some embodiments, if the face quality score is less than or equal to the first threshold, the step of obtaining the face recognition image is returned.
In some embodiments, the method for identity authentication from a face recognition image comprises: based on the face database, carrying out face matching on the face recognition image according to the face feature point data; and if the matching is successful, determining that the identity authentication is passed, and outputting the face credibility.
In some embodiments, a method of outputting face trustworthiness comprises: and obtaining the face credibility according to the face quality scores and outputting the face credibility.
Referring to fig. 2, in a specific embodiment, the method of the present invention is applied to unlocking a mobile phone. After the mobile phone is started, the mobile phone operation interface can be accessed only after face recognition authentication is required, before the mobile phone operation interface is accessed, face recognition is triggered to be executed, and the face is recognized and authenticated by starting a camera of the mobile phone and the like. Firstly, images are uploaded to a back-end analysis program in modes of shooting or selecting pictures through a mobile phone camera and the like. And the back-end analysis program stores the image pixel dot matrix corresponding to the x/y-axis digital matrix after obtaining the image, performs characteristic matching on the numerical distribution, and judges whether the human face exists or not according to the characteristic value. If the face exists, a face rectangular frame is generated, which is [ left, top, width, height ] respectively. If a plurality of faces exist, the faces are sequentially continued back to the face rectangular frame. For example, two faces are returned [ left1, top1, width1, height1, left2, top2, width2, height2 ].
And after the existence of the face is determined, continuously matching the feature values downwards to obtain face feature point data, including the number of the face feature points. In the present embodiment, 105 points are set, including the point 24 of the eyebrow, the point 32 of the eye, the point 6 of the nose, the point 34 of the mouth, and the point 9 of the outer contour. Obtaining position information of the face characteristic points through analysis, generating a face characteristic point positioning result and returning, and returning a group of face characteristic point position information for each face in the form of (x0, y0, x1, y1, … …); if a plurality of faces exist, sequentially carrying out forward extension and returning to the positioning floating point number.
In this embodiment, after the face feature point positioning result is obtained, a face quality score is obtained through analysis of the obtained face feature point positioning result, the higher the face quality score is, the higher the accuracy of face matching is, and a face with a quality score lower than 50 is determined as a low-quality face, so as to prompt a user whether to reselect an image.
In some embodiments, the specific face quality score calculation method is as follows:
1) the face ambiguity ranges from 0 to 100, and the influence value is 0.2.
The face ambiguity is calculated through the recognition number and the face direction of the face characteristic points, if the characteristic points of the left face are 52 (105 in the case of a front face, the total number of the face characteristic points is floated according to the posture), 40 face characteristic points are correctly recognized, the ambiguity value is 100 × 40/52 ═ 76.923076923, and the quality comprehensive score 76.923076923 × 0.2 ═ 15.3846153846 is calculated by combining the influence values.
2) The face accuracy is 0-100, and the influence value is 0.3.
Namely, the face credibility identification, the face credibility is obtained by checking the human feature numbers of five sense organs, such as: eyebrow recognition rate is 60%, eye recognition rate is 80%, nose recognition rate is 80%, mouth recognition rate is 70%, outer contour recognition rate is 80%, average recognition rate: 88%, the confidence value is: 88, the combined impact value yields a qualitative composite score: 88 × 0.3 ═ 26.4.
3) The upper half face of the glasses and the like is shielded, the value range is 0-100, and the influence value is 0.1.
And (3) calculating the upper half face occlusion rate through the feature points of the upper half face, if the upper half face feature points are 31 (the front face is 62, and the total number of the face feature points is floated according to the posture) in the left side face, identifying 20, and obtaining a quality comprehensive score when the upper half face occlusion score is 100 × 20/31 — 64.5161290322: 64.5161290322 0.1-6.45161290322.
4) The illumination identification influence degree is in a value range of 0-100, and the influence value is 0.1.
Obtaining illumination intensity by comprehensively taking values of an RGB channel and an HSV channel of a picture, comprehensively calculating the illumination identification influence degree by combining the obtained feature point quantity, and if the score is 80, calculating the quality comprehensive: 80 x 0.1 ═ 8.
5) The lower half face of the mask is shielded, the value range is 0-100, and the influence value is 0.1.
And (3) calculating the lower half face shielding rate through the lower half face characteristic points, if the characteristic points of the left side face are 23 (the front face is 49, the total number of the face characteristic points floats according to the posture), correctly identifying 23 characteristic points, and calculating the lower half face shielding fraction to be 100 to obtain a quality comprehensive fraction: 100 x 0.1 ═ 10.
6) The picture noise influence is in a value range of 0-100, and the influence value is 0.1.
And identifying human face characteristic points according to the original image and the de-noised image, comparing the human face characteristic points (noise processing comprises mean filtering, Gaussian filtering, median filtering, bilinear filtering and an edge detection algorithm), and obtaining a noise influence value, wherein if the identification characteristic points of the original image are 80 and the identification characteristic points of the de-noised image are 90, the noise influence rate is 100 × 80/90 to 88.88888888, the obtained influence value is 88.88888888, and the quality comprehensive score is 8.888888888.
7) Attitude influence, the value range is 0-100, and the influence value is 0.1.
And obtaining a posture influence score according to the posture, wherein if the posture influence is 50 percent (the recognition sample only has 50 percent of the front face) in the case of the left side face, the score is 50, and the quality comprehensive score is 5 through calculation.
The face quality score calculation result is the sum of the 7 scores, and the quality comprehensive score when the example is the left face is as follows:
15.3846153846+26.4+6.45161290322+8+10+8.888888888+5＝80.12511718。
after the face quality score is obtained, whether the current picture meets the requirement is judged according to a preset threshold value, if yes, the face is matched through the face features, the identity is recognized, a recognition result is obtained, the corresponding matching face credibility (the value range is 0-100) is returned, the higher the credibility is, the higher the accuracy is, the credibility is influenced by the face quality score, and if no matching face exists, the user is prompted whether to reselect the image.
Referring to fig. 3, in a specific embodiment, the method of this embodiment is applied to a scene that needs to enter user information, such as new user registration. After the basic information of the user is input, a camera is called to obtain an image to be recognized by sending a face recognition request, and the obtained image to be recognized is subjected to identity authentication according to the method of the embodiment. It is understood that the face recognition request may be triggered by a specific event, for example, if the camera captures face information, the face recognition is triggered to be executed, and the face recognition authentication is automatically entered.
Referring to fig. 4, in some embodiments, an identity authentication apparatus based on face recognition is provided in an embodiment of the present invention, including: the first module is used for acquiring a face recognition image, carrying out image analysis on the face recognition image and judging whether a face exists or not; the second module is used for acquiring face feature point data when the first module determines that the face identification image has a face; the third module is used for calculating a quality factor according to the face feature point data and obtaining a face quality score according to the quality factor and a corresponding influence value; the fourth module is used for carrying out identity authentication according to the face recognition image when the face quality score is larger than the first threshold value; wherein the quality factor comprises at least one of: the human face fuzzy degree, the human face accuracy, the upper half face shielding rate, the lower half face shielding rate, the illumination recognition influence degree, the image noise influence value and the posture influence value.
Referring to fig. 5, an embodiment of the present invention provides an electronic device, including a processor and a memory; the memory is used for storing programs; the processor executes the program to implement the foregoing method. The electronic device may be a Personal Computer (PC), a mobile phone, a smart phone, a Personal Digital Assistant (PDA), a wearable device, a pocket PC, a tablet PC, or the like.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the foregoing method.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The processor of the computer device may read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to execute the aforementioned identity authentication method based on face recognition.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An identity authentication method based on face recognition is characterized by comprising the following steps:
acquiring a face recognition image, carrying out image analysis on the face recognition image, and judging whether a face exists or not;
if the human face exists, obtaining human face feature point data;
calculating a quality factor according to the face feature point data, and obtaining a face quality score according to the quality factor and a corresponding influence value;
if the face quality score is larger than a first threshold value, performing identity authentication according to the face recognition image;
wherein the quality factor comprises at least one of: the method comprises the following steps of human face ambiguity, human face accuracy, upper half face shielding rate, lower half face shielding rate, illumination identification influence degree, image noise influence value and posture influence value.
2. The identity authentication method based on face recognition according to claim 1, wherein the obtaining of the face recognition image, the image analysis of the face image, and the determining whether the face exists comprise:
acquiring a face recognition image and storing the face recognition image through an xy-axis data matrix corresponding to an image pixel dot matrix to obtain face recognition data;
and carrying out feature matching on the numerical distribution of the face identification data, and judging whether a face exists or not according to the feature value.
3. The identity authentication method based on face recognition according to claim 1, wherein the obtaining of the face feature point data comprises:
acquiring the number and position coordinates of face characteristic points corresponding to each face; the face feature points at least comprise: eyebrows, eyes, nose, mouth, and outer contour.
4. The identity authentication method based on face recognition according to claim 1, wherein the calculating quality factors according to the face feature point data comprises one or more of the following:
calculating the face ambiguity according to the number of the face characteristic points and the face direction;
detecting the number of facial feature points of five sense organs to obtain the face credibility;
calculating the number of the face characteristic points of the upper half face to obtain the upper half face shielding rate;
obtaining illumination intensity by comprehensively taking values of an RGB channel and an HSV channel of the face recognition image, and obtaining illumination recognition influence degree by combining the number of the face characteristic points;
calculating the number of the face characteristic points of the lower half face to obtain the shielding rate of the lower half face;
acquiring first face characteristic point data identified by the face recognition image and second face characteristic point data identified by the de-noised face recognition image, and comparing the first face characteristic point data with the second face characteristic point data to obtain an image noise influence value; and
and carrying out gesture recognition on the face recognition image to obtain a gesture influence score.
5. The identity authentication method based on face recognition according to claim 1, wherein if the face quality score is less than or equal to the first threshold, the step of obtaining the face recognition image is returned.
6. The identity authentication method based on the face recognition according to claim 1, wherein the identity authentication according to the face recognition image comprises:
based on a face database, carrying out face matching on the face recognition image according to the face feature point data;
and if the matching is successful, determining that the identity authentication is passed, and outputting the face credibility.
7. The identity authentication method based on face recognition according to claim 6, wherein the outputting the face credibility comprises:
and obtaining the face credibility according to the face quality scores and outputting the face credibility.
8. An identity authentication device based on face recognition is characterized by comprising:
the system comprises a first module, a second module and a third module, wherein the first module is used for acquiring a face recognition image, carrying out image analysis on the face recognition image and judging whether a face exists or not;
the second module is used for acquiring face feature point data when the first module determines that the face identification image has a face;
the third module is used for calculating a quality factor according to the face feature point data and obtaining a face quality score according to the quality factor and a corresponding influence value;
the fourth module is used for carrying out identity authentication according to the face recognition image when the face quality score is larger than a first threshold value;
wherein the quality factor comprises at least one of: the human face fuzzy degree, the human face accuracy, the upper half face shielding rate, the lower half face shielding rate, the illumination recognition influence degree, the image noise influence value and the posture influence value.
9. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executing the program realizes the method of any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 7.
Priority Applications (1)
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| CN117371785A (en) * | 2023-10-09 | 2024-01-09 | 湖南荣安数字技术科技有限公司 | Safety judgment method based on multidimensional factor identity recognition and escort service system |
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| CN117371785A (en) * | 2023-10-09 | 2024-01-09 | 湖南荣安数字技术科技有限公司 | Safety judgment method based on multidimensional factor identity recognition and escort service system |
| CN117371785B (en) * | 2023-10-09 | 2024-03-12 | 湖南荣安数字技术科技有限公司 | Safety judgment method based on multidimensional factor identity recognition and escort service system |
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