CN111507165A - Face recognition method and device, electronic equipment and computer readable storage medium - Google Patents
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Abstract
The application provides a face recognition method, a face recognition device, an electronic device and a computer readable storage medium, wherein the method comprises the following steps: after a face recognition request is obtained, a video to be recognized is obtained through a terminal camera; determining current facial feature information and current action feature information according to a video to be identified; judging whether the database stores standard facial feature information and standard action feature information or not; and if the database stores the standard facial feature information and the standard action feature information, determining that the face recognition is passed. The method and the device can identify the face video by judging whether the database stores the standard face feature information of which the similarity with the current face feature information reaches a first preset similarity threshold or not and whether the database stores the standard action feature information of which the similarity with the current action feature information reaches a second preset similarity threshold or not, so that the safety of face identification can be improved.
Description
Technical Field
The present application relates to the field of information technology, and in particular, to a face recognition method, an apparatus, an electronic device, and a computer-readable storage medium.
Background
With the rapid progress of science and technology, the user needs to be authenticated under various conditions, wherein the face recognition mode is gradually and widely applied. Face recognition is a biometric technology that automatically performs identity recognition based on facial features of a person (e.g., statistical or geometric features), and is also known as face recognition, portrait recognition, face recognition, and the like. Generally, the face recognition is called as short for identification and verification based on optical face images. The face recognition uses a camera or a video camera to collect images or video streams containing faces, automatically detects and tracks the faces in the images, and further performs a series of related application operations on the detected face images. Techniques include image acquisition, feature localization, identity verification and lookup, and the like. In short, the features in the human face, such as the height of eyebrows, the corners of the mouth, and the like, are extracted from the photo, and the result is output through comparison of the features.
However, the reliability of the verification result obtained by identifying the face only by the facial features is not high, and the security is low.
Disclosure of Invention
In view of the above, the present application provides a face recognition method, a face recognition apparatus, an electronic device and a computer-readable storage medium, so as to improve the security of face recognition.
In a first aspect, an embodiment of the present application provides a face recognition method, including:
after a face recognition request is obtained, a video to be recognized is obtained through a terminal camera;
determining current facial feature information and current action feature information according to the video to be identified;
judging whether the database stores standard facial feature information and standard action feature information or not; the standard facial feature information and the standard motion feature information are determined from the same standard video; the similarity between the current facial feature information and the standard facial feature information reaches a first preset similarity threshold; the similarity between the current action characteristic information and the standard action characteristic information reaches a second preset similarity threshold;
and if so, determining that the face recognition is passed.
With reference to the first aspect, an embodiment of the present application provides a first possible implementation manner of the first aspect, where after determining that face recognition passes, the method further includes:
a transaction request is sent to a transaction server.
With reference to the first aspect, an embodiment of the present application provides a second possible implementation manner of the first aspect, where the determining whether the database stores standard facial feature information and standard motion feature information includes:
judging whether standard facial feature information is stored in a database; the similarity between the current facial feature information and the standard facial feature information reaches a first preset similarity threshold;
if yes, judging whether standard action characteristic information is stored in the database; the similarity between the current action characteristic information and the standard action characteristic information reaches a second preset similarity threshold; the standard facial feature information and the standard motion feature information are determined from the same standard video.
With reference to the first aspect, the first possible implementation manner, or the second possible implementation manner, an embodiment of the present application provides a third possible implementation manner of the first aspect, wherein the standard action characteristic information includes at least one or more of the following: blinking, nodding, shaking, frowning, and smiling.
In a second aspect, an embodiment of the present application further provides a face recognition apparatus, including:
the acquisition module is used for acquiring a video to be identified through a terminal camera after the face identification request is acquired;
the first determining module is used for determining current facial feature information and current action feature information according to the video to be identified;
the judging module is used for judging whether the database stores standard facial feature information and standard action feature information or not; the standard facial feature information and the standard motion feature information are determined from the same standard video; the similarity between the current facial feature information and the standard facial feature information reaches a first preset similarity threshold; the similarity between the current action characteristic information and the standard action characteristic information reaches a second preset similarity threshold;
and the second determination module is used for determining that the face recognition is passed if the face recognition is passed.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine-readable instructions being executable by the processor to perform the steps of any one of the possible implementations of the first aspect.
In a fourth aspect, this application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to perform the steps in any one of the possible implementation manners of the first aspect.
According to the face recognition method provided by the embodiment of the application, firstly, after a face recognition request is obtained, a video to be recognized is obtained through a terminal camera; then, determining current facial feature information and current action feature information according to the video to be identified; then, judging whether the database stores standard facial feature information and standard action feature information or not; and finally, if the database stores the standard facial feature information and the standard action feature information, determining that the face recognition is passed. The method and the device can identify the face video by judging whether the database stores the standard facial feature information of which the similarity with the current facial feature information reaches a first preset similarity threshold or not and whether the database stores the standard motion feature information of which the similarity with the current motion feature information reaches a second preset similarity threshold or not.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 shows a flowchart of a face recognition method provided in an embodiment of the present application;
fig. 2 is a flowchart illustrating another face recognition method provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram illustrating a face recognition apparatus according to an embodiment of the present application;
fig. 4 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a face recognition method, which is described below through an embodiment. As shown in the flow chart of the face recognition method in fig. 1, the method includes the following steps:
s101: and after the face recognition request is acquired, acquiring the video to be recognized through a terminal camera.
In the step, after a face recognition request is received, the interrupt camera can be controlled to acquire a video to be recognized, and image information in the video to be recognized is converted into digital information, wherein the video to be recognized comprises a video clip of the face of a user to be recognized.
S102: and determining current facial feature information and current action feature information according to the video to be identified.
In this step, the video to be identified in the form of digital signal may be stored in a BMP (bitmap) format, and the digital information in the BMP format is grayed to obtain a grayscale image of each frame image of the video to be identified, a grayscale value having an equal R value, G value, and B value is selected, and the selected grayscale value is in the range of (0, 255), so as to convert the grayscale image into black and white, reduce the information amount of the image, and reduce the size of the image.
Further, after the video to be recognized is converted into a black-white gray image, the video to be recognized can be divided by using a threshold value method, the gray image is divided into two parts according to gray levels, the difference between the gray levels of the two parts is made to be maximum, the difference between the gray levels of the two parts is made to be minimum, and a proper gray level can be searched through variance calculation for division.
Specifically, the division may be performed by using a maximum inter-class variance method (OTSU), and the division value of the current grayscale image may be set to t, the ratio of foreground points to images is W0, the mean value is U0, the ratio of background points to images is W1, and the mean value is U1. The average of the entire image is U-W0U 0+ W1U 1.
Based on the above formula, the objective function g (t) ═ W0 ^ W0-U2 + W1 ^ U1-U2 can be established. The objective function g (t) is an expression of the inter-class variance when the current segmentation threshold is t.
After the image of the video to be identified is binarized, morphological processing can be carried out on the image, firstly, the image is subjected to expansion processing, and all points contacted by an object in the image can be combined with a background in the step; then, carrying out retest processing on the image, corroding spots in the image and eliminating bulges in the image; then, the broken portion, the rough portion, the boundary portion, and the uneven portion of the image may be processed to improve the recognizability of the image.
And finally, scanning each pixel in the image through a preset facial feature extraction template, replacing the value of a central pixel point of the template by using the weighted average gray value of the pixels in the neighborhood determined by the facial feature extraction template, and extracting the current facial feature information.
After the current facial feature information is obtained, time normalization processing can be performed on the processed video to be recognized, time is taken as a uniform coordinate axis, time warping processing is performed on the current facial feature information by using a DTW (dynamic time warping) algorithm, facial expressions and facial movements are obtained, and then current movement feature information is extracted.
S103: judging whether the database stores standard facial feature information and standard action feature information or not; the standard facial feature information and the standard motion feature information are determined from the same standard video; the similarity between the current facial feature information and the standard facial feature information reaches a first preset similarity threshold; and the similarity between the current action characteristic information and the standard action characteristic information reaches a second preset similarity threshold value.
In this step, it may be determined whether the database stores standard facial feature information and standard motion feature information, and if so, the similarity between the current facial feature information and the standard facial feature information and the similarity between the current motion feature information and the standard motion feature information may be determined.
Specifically, it can be determined that the features such as visual features, pixel statistical features, face image algebraic features, euclidean distance, curvature, angle, and the like in the current facial feature information are compared with each feature in the standard facial feature information.
When the similarity between the current action characteristic information and the standard action characteristic information is determined, after time regularization processing is carried out, the motion direction of the facial features of a user can be judged, the lengths of two time sequences Q and C in the current action characteristic information and the standard action characteristic information can be set to be n and m respectively, so that the sequence of the standard action characteristic information is used as a reference template, and the sequence of the current action characteristic information is used as a test template; if m is equal to n, directly calculating the distance between two sequences, if m and n are not equal, carrying out linear scaling, linearly amplifying the short sequence to be as long as the long sequence for comparison, or linearly shortening the long sequence to be as long as the short sequence for comparison. Aligning the two sequences requires constructing a network of matrices of n, x, m, the matrix elements representing the distance of the two points. And determining the similarity between the current action characteristic information and the standard action characteristic information according to the distance between the two points.
The first preset similarity threshold may be 80%, and the second preset similarity threshold may be 80%.
The standard facial feature information and the standard action feature information may be one or more, the form of the standard facial feature information and the standard action feature information may be a template form, when a plurality of standard facial feature information and standard action feature information exist in the database, the similarity may be determined one by one, and the method stops when the similarity is higher than a first preset similarity threshold and a second similarity threshold respectively, and outputs a result.
S104: and if so, determining that the face recognition is passed.
In this step, if the determination results in step S103 are all yes, it is determined that the face recognition is passed.
In a possible implementation, after determining that the face recognition passes, the method further includes:
a transaction request is sent to a transaction server.
In the step, if the face recognition is passed, the user to be recognized is determined to be the user himself, and a transaction request is sent to a transaction server.
In one possible implementation, the determining whether the database stores standard facial feature information and standard motion feature information includes:
judging whether standard facial feature information is stored in a database; the similarity between the current facial feature information and the standard facial feature information reaches a first preset similarity threshold;
if yes, judging whether standard action characteristic information is stored in the database; the similarity between the current action characteristic information and the standard action characteristic information reaches a second preset similarity threshold; the standard facial feature information and the standard motion feature information are determined from the same standard video.
In one possible implementation, the standard action characteristic information includes at least one or more of the following: blinking, nodding, shaking, frowning, and smiling.
Based on the same technical concept, embodiments of the present application further provide a face recognition apparatus, an electronic device, a computer-readable storage medium, and the like, which can be seen in the following embodiments.
Fig. 2 shows a flow chart of a face recognition method according to some embodiments of the present application, as shown in fig. 2, the face recognition method pre-processes a video to be recognized, then, extracting the features to obtain the current facial feature information, matching the standard facial feature information in the database, when the similarity is greater than a first preset similarity threshold, the current facial feature information is processed by normalization processing and time planning algorithm processing to obtain the current motion feature information, and the motion direction is judged, then comparing with the standard action characteristic information, determining whether the similarity is greater than a second preset similarity threshold, outputting the result, if the authentication condition is not met, and loading the next template (standard facial feature information and standard action feature information) for cycle matching until the matching is successful or the cycle number exceeds a preset number threshold.
The preprocessing comprises face image acquisition, laser radar penetration processing, face detection, Adaboost algorithm processing, gray level correction, noise filtering, optical fiber compensation, gray level transformation, histogram equalization, normalization, geometric correction, filtering and sharpening; feature extraction may include extracting visual features, pixel statistics, face image algebra features, Euclidean distance features, curvature and angle features.
Fig. 3 is a block diagram illustrating a face recognition apparatus according to some embodiments of the present application, which implements functions corresponding to the above-described steps of executing a face recognition method on a terminal device. The apparatus may be understood as a component of a server including a processor, which is capable of implementing the above-mentioned face recognition method, as shown in fig. 3, the face recognition apparatus may include:
the acquiring module 301 is configured to acquire a video to be identified through a terminal camera after acquiring the face identification request;
a first determining module 302, configured to determine current facial feature information and current motion feature information according to the video to be identified;
a judging module 303, configured to judge whether the database stores standard facial feature information and standard motion feature information; the standard facial feature information and the standard motion feature information are determined from the same standard video; the similarity between the current facial feature information and the standard facial feature information reaches a first preset similarity threshold; the similarity between the current action characteristic information and the standard action characteristic information reaches a second preset similarity threshold;
and a second determining module 304, configured to determine that the face recognition is passed if the face recognition is successful.
In a possible implementation, the face recognition apparatus may further include:
and the sending module is used for sending the transaction request to the transaction server.
In one possible implementation, the determining module 303 includes:
the first judgment submodule is used for judging whether the database stores standard facial feature information or not; the similarity between the current facial feature information and the standard facial feature information reaches a first preset similarity threshold;
the second judgment submodule is used for judging whether the database stores standard action characteristic information or not if the database stores the standard action characteristic information; the similarity between the current action characteristic information and the standard action characteristic information reaches a second preset similarity threshold; the standard facial feature information and the standard motion feature information are determined from the same standard video.
In one possible embodiment, the standard action characteristic information includes at least one or more of: blinking, nodding, shaking, frowning, and smiling.
As shown in fig. 4, which is a schematic structural diagram of an electronic device 400 provided in an embodiment of the present application, the electronic device 400 includes: at least one processor 401, at least one network interface 404 and at least one user interface 403, memory 405, at least one communication bus 402. A communication bus 402 is used to enable connective communication between these components. The user interface 403 includes a display (e.g., a touch screen), a keyboard, or a pointing device (e.g., a touch pad or touch screen, etc.).
In some embodiments, memory 405 stores the following elements, executable modules or data structures, or a subset thereof, or an expanded set thereof:
an operating system 4051, which contains various system programs, for implementing various basic services and processing hardware-based tasks;
the application 4052 includes various applications for implementing various application services.
In an embodiment of the present application, processor 401, by invoking programs or instructions stored by memory 405, is configured to:
after a face recognition request is obtained, a video to be recognized is obtained through a terminal camera;
determining current facial feature information and current action feature information according to the video to be identified;
judging whether the database stores standard facial feature information and standard action feature information or not; the standard facial feature information and the standard motion feature information are determined from the same standard video; the similarity between the current facial feature information and the standard facial feature information reaches a first preset similarity threshold; the similarity between the current action characteristic information and the standard action characteristic information reaches a second preset similarity threshold;
and if so, determining that the face recognition is passed.
In one possible implementation, the processor 401 is further configured to: and after the face recognition is determined to pass, sending a transaction request to a transaction server.
In one possible implementation, the processor 401 is further configured to: judging whether standard facial feature information is stored in a database; the similarity between the current facial feature information and the standard facial feature information reaches a first preset similarity threshold;
if yes, judging whether standard action characteristic information is stored in the database; the similarity between the current action characteristic information and the standard action characteristic information reaches a second preset similarity threshold; the standard facial feature information and the standard motion feature information are determined from the same standard video.
In one possible embodiment, the standard action characteristic information includes at least one or more of: blinking, nodding, shaking, frowning, and smiling.
The computer program product for performing the face recognition method provided in the embodiment of the present application includes a computer-readable storage medium storing a nonvolatile program code executable by a processor, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, and is not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
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 non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including 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 application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. The face recognition method is characterized by comprising the following steps:
after a face recognition request is obtained, a video to be recognized is obtained through a terminal camera;
determining current facial feature information and current action feature information according to the video to be identified;
judging whether the database stores standard facial feature information and standard action feature information or not; the standard facial feature information and the standard motion feature information are determined from the same standard video; the similarity between the current facial feature information and the standard facial feature information reaches a first preset similarity threshold; the similarity between the current action characteristic information and the standard action characteristic information reaches a second preset similarity threshold;
and if so, determining that the face recognition is passed.
2. The face recognition method of claim 1, after determining that the face recognition is passed, further comprising:
a transaction request is sent to a transaction server.
3. The method of claim 1, wherein the determining whether the database stores standard facial feature information and standard motion feature information comprises:
judging whether standard facial feature information is stored in a database; the similarity between the current facial feature information and the standard facial feature information reaches a first preset similarity threshold;
if yes, judging whether standard action characteristic information is stored in the database; the similarity between the current action characteristic information and the standard action characteristic information reaches a second preset similarity threshold; the standard facial feature information and the standard motion feature information are determined from the same standard video.
4. The face recognition method according to any one of claims 1 to 3, wherein the standard action feature information includes at least one or more of the following: blinking, nodding, shaking, frowning, and smiling.
5. A face recognition apparatus, comprising:
the acquisition module is used for acquiring a video to be identified through a terminal camera after the face identification request is acquired;
the first determining module is used for determining current facial feature information and current action feature information according to the video to be identified;
the judging module is used for judging whether the database stores standard facial feature information and standard action feature information or not; the standard facial feature information and the standard motion feature information are determined from the same standard video; the similarity between the current facial feature information and the standard facial feature information reaches a first preset similarity threshold; the similarity between the current action characteristic information and the standard action characteristic information reaches a second preset similarity threshold;
and the second determination module is used for determining that the face recognition is passed if the face recognition is passed.
6. The face recognition apparatus according to claim 5, further comprising:
and the sending module is used for sending the transaction request to the transaction server.
7. The face recognition apparatus of claim 5, wherein the determining module comprises:
the first judgment submodule is used for judging whether the database stores standard facial feature information or not; the similarity between the current facial feature information and the standard facial feature information reaches a first preset similarity threshold;
the second judgment submodule is used for judging whether the database stores standard action characteristic information or not if the database stores the standard action characteristic information; the similarity between the current action characteristic information and the standard action characteristic information reaches a second preset similarity threshold; the standard facial feature information and the standard motion feature information are determined from the same standard video.
8. The apparatus according to any one of claims 5 to 7, wherein the standard motion characteristic information comprises at least one or more of: blinking, nodding, shaking, frowning, and smiling.
9. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is operating, the machine-readable instructions when executed by the processor performing the steps of the face recognition method according to any one of claims 1 to 4.
10. Computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the face recognition method according to one of claims 1 to 4.
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