CN108280422B - Method and device for recognizing human face - Google Patents
Method and device for recognizing human face Download PDFInfo
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- CN108280422B CN108280422B CN201810059846.0A CN201810059846A CN108280422B CN 108280422 B CN108280422 B CN 108280422B CN 201810059846 A CN201810059846 A CN 201810059846A CN 108280422 B CN108280422 B CN 108280422B
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- 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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Abstract
The embodiment of the application discloses a method and a device for recognizing a human face. One embodiment of the method comprises: acquiring an image of a user to be subjected to identity authentication; inquiring images of which the similarity between the face object contained in at least one image group and the face object contained in the acquired images exceeds a preset threshold; determining an image group where the inquired image is located as a target image group, and determining whether the quality data of the acquired image is matched with a preset data range; in response to determining that the quality data matches the preset data range, adding the acquired image to the target image group. The embodiment improves the efficiency of face recognition.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a method and a device for recognizing human faces.
Background
The face recognition technology is an artificial intelligence technology that verifies the identity of a user based on facial features of the user. At present, the commonly adopted mode is as follows: the method comprises the steps of obtaining an image of a user to be subjected to identity verification, finding out a registered image with the highest similarity between a face object contained in a face recognition system and the face object in the collected image of the user to be subjected to identity verification, and passing the identity verification of the user to be subjected to identity verification when the highest similarity is larger than a similarity threshold.
Disclosure of Invention
The embodiment of the application provides a method and a device for recognizing a human face.
In a first aspect, an embodiment of the present application provides a method for recognizing a human face, where the method includes: acquiring an image of a user to be subjected to identity authentication; inquiring images of which the similarity between the face object contained in at least one image group and the face object contained in the acquired images exceeds a preset threshold; determining an image group where the inquired image is located as a target image group, and determining whether the quality data of the acquired image is matched with a preset data range; in response to determining that the quality data matches the preset data range, adding the acquired image to the target image group.
In some embodiments, the target image group includes a registration image and a late join image; and inquiring images of which the similarity between the face object contained in at least one image group and the face object contained in the acquired images exceeds a preset threshold, wherein the image comprises the following steps: and inquiring a registered image of which the similarity between the face object contained in at least one image group and the face object contained in the acquired image exceeds a first preset threshold, or inquiring a post-added image of which the similarity between the face object contained in at least one image group and the face object contained in the acquired image exceeds a second preset threshold.
In some embodiments, the second preset threshold is greater than the first preset threshold.
In some embodiments, querying images in which the similarity between a face object included in at least one image group and a face object included in the acquired image exceeds a preset threshold comprises: determining an image group of which the similarity between a face object contained in a registered image and a face object contained in a later-added image is greater than a third preset threshold value; and querying the post-added image of which the similarity between the face object contained in the determined image group and the face object contained in the acquired image exceeds a second preset threshold.
In some embodiments, determining whether the quality data of the acquired image matches a preset data range comprises: it is determined whether the brightness and sharpness of the acquired image exceed a fourth preset threshold.
In a second aspect, an embodiment of the present application provides an apparatus for recognizing a human face, where the apparatus includes: the system comprises an acquisition unit, a verification unit and a verification unit, wherein the acquisition unit is used for acquiring an image of a user to be subjected to identity verification; the query unit is used for querying images, the similarity between the face object contained in at least one image group and the face object contained in the acquired images exceeds a preset threshold value; the determining unit is used for determining an image group where the inquired image is located as a target image group and determining whether the quality data of the acquired image is matched with a preset data range; an adding unit for adding the acquired image to the target image group in response to determining that the quality data matches the preset data range.
In some embodiments, the target image group includes a registration image and a late join image; and a querying unit, further configured to: and inquiring a registered image of which the similarity between the face object contained in at least one image group and the face object contained in the acquired image exceeds a first preset threshold value, or inquiring a post-added image of which the similarity between the face object contained in at least one image group and the face object contained in the acquired image exceeds a second preset threshold value.
In some embodiments, the second preset threshold is greater than the first preset threshold.
In some embodiments, a query unit, comprises: the determining subunit is used for determining an image group of which the similarity between the face object contained in the registered image and the face object contained in the later-added image is greater than a third preset threshold value; and the query subunit is used for querying the post-added image of which the similarity between the face object contained in the determined image group and the face object contained in the acquired image exceeds a second preset threshold.
In some embodiments, the determining unit is further configured to: it is determined whether the brightness and sharpness of the acquired image exceed a fourth preset threshold.
In a third aspect, an embodiment of the present application provides an apparatus, including: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method as described above in relation to the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, wherein the computer program is configured to, when executed by a processor, implement the method as described above in the first aspect.
According to the method and the device for recognizing the human face, the image of the user to be subjected to identity verification is acquired, the image with the similarity between the human face object contained in at least one image group and the human face object contained in the acquired image exceeding the preset threshold is inquired, the image group where the inquired image is located is determined as the target image group, whether the quality data of the acquired image is matched with the preset data range or not is determined, and finally the acquired image is added into the target image group in response to the fact that the quality data is matched with the preset data range, so that the efficiency of human face recognition is improved.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for recognizing a human face according to the present application;
FIG. 3 is a schematic diagram of an application scenario of the method for recognizing a human face according to the present application;
FIG. 4 is a flow diagram of yet another embodiment of a method for recognizing a human face according to the present application;
FIG. 5 is a schematic block diagram of an embodiment of an apparatus for recognizing a human face according to the present application;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing a server according to embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows an exemplary system architecture 100 to which embodiments of the present method for recognizing a human face or apparatus for recognizing a human face may be applied.
As shown in fig. 1, the system architecture may include a gate 101, a terminal 102, a gate 103, a network 104, and servers 105, 106. The terminal devices 101 and 102 may be various electronic devices with cameras, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, desktop computers, and the like.
The terminal device 101, the terminal device 102 and the gate machine 103 can acquire images of a user to be subjected to identity verification through a camera, and then inquire images of which the similarity between a face object contained in at least one image group and a face object contained in the acquired images exceeds a preset threshold; determining an image group where the inquired image is located as a target image group, and determining whether the quality data of the acquired image is matched with a preset data range; in response to determining that the quality data matches the preset data range, adding the acquired image to the target image group.
The terminal device 101, the terminal device 102, and the gate 103 may also acquire an image of a user to be subjected to identity verification through a camera, and then send the acquired image to the servers 105 and 106 through the network 104, where the servers 105 and 106 may be servers providing various services, such as a background server providing support for a face recognition application running on the terminal device 101, the terminal device 102, and the gate 103, and after the servers 105 and 106 acquire the image of the user to be subjected to identity verification, the servers 105 and 106 may query an image in which a similarity between a face object included in at least one image group and a face object included in the acquired image exceeds a preset threshold; determining an image group where the inquired image is located as a target image group, and determining whether the quality data of the acquired image is matched with a preset data range; in response to determining that the quality data matches the preset data range, adding the acquired image to the target image group.
It should be noted that the method for recognizing a human face provided in the embodiment of the present application may be executed by the terminal device 101, the terminal device 102, the gate 103, or the servers 105 and 106, and accordingly, the apparatus for recognizing a human face may be disposed in the terminal device 101, the terminal device 102, the gate 103, or the servers 105 and 106.
It should be understood that the number of terminal devices, gates, networks, and servers in fig. 1 are merely illustrative. There may be any number of terminal devices, gates, networks, and servers, as desired for implementation.
With continued reference to fig. 2, a flow 200 of one embodiment of a method for recognizing a human face in accordance with the present application is shown. The method for recognizing the human face comprises the following steps:
In this embodiment, an electronic device (for example, the electronic device shown in fig. 1) on which the method for recognizing a human face is executed may acquire an image of a user to be authenticated through a camera to obtain an image of the user to be authenticated. Any person whose image may be captured by a face recognition system for authentication may be referred to as a user.
In this embodiment, the identity of the user may be verified through a face recognition system. As an example, when a user arrives near a gate running a face recognition system, the user needs to be authenticated by the face recognition system to pass the gate. The user may be photographed first and an image of the user may be captured.
In this embodiment, the electronic device may query an image, in which the similarity between the face object included in at least one image group and the face object included in the image acquired in step 201 exceeds a preset threshold. Each image group of the at least one image group may be associated with a user identification, and images of the image group in the face recognition system include face objects of the user. The face of the user in the image may be referred to as a face object corresponding to the face of the user. For the face object in the image, when only the face object corresponding to the face of the user is included in one image, the image may be referred to as the image of the user. For example, the image of the user may be a certificate photo that only contains a face object corresponding to the face of the user.
In this embodiment, if an image in which the similarity between the face object included in the at least one image group and the face object included in the acquired image exceeds a preset threshold is queried, it may also be determined that the user to be subjected to the identity verification passes the identity verification, and the user identifier in the face recognition system is the user identifier associated with the queried image.
In this embodiment, the similarity may be the similarity of the features of two face objects. The features of the human face object can be represented by facial feature points. When calculating the similarity of two face objects, feature point vectors corresponding to the facial feature points of the two face objects can be respectively generated, then the cosine similarity of the generated two feature point vectors is calculated, and the cosine similarity is used as the similarity of the two face objects. The feature vectors of the face objects can also be obtained through the following steps, and then the similarity of the two face objects is obtained based on a model trained by a machine learning algorithm: and segmenting the image to obtain the region where the face object is located, and then acquiring the characteristics of the face object through a convolutional neural network.
The preset threshold may be set according to actual needs, for example, a higher threshold may be set when the image quality is higher. The preset threshold may also be a variable value associated with a predefined setting rule, for example, the similarity between the face object included in one or more images included in each image group in at least one image group and the face object included in the acquired image may be calculated respectively, the similarity is sorted in descending order of similarity, and the similarity ranked next is determined as the preset threshold.
In some optional implementations of this embodiment, the target image group includes a registered image and a late joining image; and inquiring images of which the similarity between the face object contained in at least one image group and the face object contained in the acquired images exceeds a preset threshold, wherein the image comprises the following steps: and inquiring a registered image of which the similarity between the face object contained in at least one image group and the face object contained in the acquired image exceeds a first preset threshold, or inquiring a post-added image of which the similarity between the face object contained in at least one image group and the face object contained in the acquired image exceeds a second preset threshold.
In this implementation manner, the registration image refers to an image that is actively submitted or determined by the user, and may refer to an image of the user that is acquired in advance before the face recognition system performs identity verification on the user, for example, an image of the user that is submitted when the user registers in the face recognition system, so that the registration establishes a correspondence between the image submitted by the user and an account of the user, and the user may subsequently log in the face recognition system to modify the registration image. The second preset threshold and the first preset threshold can be set according to actual needs, for example, for a gate, the preset threshold can be correspondingly reduced when the passing rate is high.
As an example, before the gate on which the method for recognizing a human face operates first authenticates a user, an image of the user, for example, a certificate of the user, may be sent to the server by the terminal of the user, and then the server sends the image of the user to the gate. The face recognition system running on the gate can receive the image of the user, create an image group corresponding to the user, and add the received image of the user as a registration image into the created image group corresponding to the user.
The post-join image may be an image belonging to a user other than the registered image in an image group corresponding to the user, for example, an image collected by the user every time the user passes the authentication in the historical authentication process and added to the image group corresponding to the user.
In some optional implementations of this embodiment, the second preset threshold is greater than the first preset threshold. Because the acquisition time of the later-added image is closer to the time for performing identity verification, and the angle and the environment for image shooting are also more similar, the similarity between the human face object included in the later-added image and the human face object included in the image for acquiring the user to be subjected to identity verification is larger, so that the second preset threshold value can be larger than the first preset threshold value.
In this embodiment, in response to the image in which the similarity between the face object included in the query in step 202 and the face object included in the acquired image exceeds the preset threshold, the electronic device may determine the image group in which the query image is located as the target image group, and determine whether the quality data of the acquired image matches the preset data range. The target image group may be considered as a group of images associated with a user to be authenticated, which is determined when the user is authenticated.
The quality data may include brightness and sharpness of the image and data for characterizing the pose of the face object in the image, generally, the quality of the front face image is better than that of the side face image, and the data for characterizing the pose of the face object in the image may be obtained via a model trained by a machine learning algorithm.
In some optional implementations of the embodiment, determining whether the quality data of the acquired image matches a preset data range includes: it is determined whether the brightness and sharpness of the acquired image exceed a fourth preset threshold. The fourth preset threshold value can be set according to actual needs, and the brightness and the definition of the image reach the fourth preset threshold value, so that correct face features can be extracted, and similarity calculation can be performed.
In this embodiment, the electronic device may add the acquired image to the target image group in response to determining that the quality data matches the preset data range in step 203. The acquired images are newly added into the target image group, and when identity authentication is subsequently performed on the user, the original images in the image group and the newly added images can be used as the basis.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the method for recognizing a human face according to the present embodiment. In the application scenario of fig. 3, a camera 302 acquires an image 303 of a user to be subjected to identity authentication, and sends the acquired image 303 to an electronic device 301, the electronic device 301 acquires the image 303 of the user to be subjected to identity authentication, and queries an image in which a similarity between a face object included in at least one image group 304 and a face object included in the acquired image 303 exceeds a preset threshold; in response to the query of an image containing a face object whose similarity with a face object contained in the acquired image 303 exceeds a preset threshold, determining an image group in which the queried image is located as a target image group 305, and determining whether quality data of the acquired image matches a preset data range; in response to determining that the quality data matches the preset data range, the acquired images are added to the target image group 305.
The method provided by the embodiment of the application acquires the image of the user to be subjected to identity authentication; inquiring images of which the similarity between the face object contained in at least one image group and the face object contained in the acquired images exceeds a preset threshold; determining an image group where the inquired image is located as a target image group, and determining whether the quality data of the acquired image is matched with a preset data range; and in response to the fact that the quality data are matched with the preset data range, adding the acquired images into the target image group, wherein the facial features of the user represented by the later-added collected images are closest to the current facial features of the user, so that the face can be identified more quickly and accurately subsequently, and the face identification efficiency is improved.
With further reference to fig. 4, a flow 400 of yet another embodiment of a method for recognizing a human face is shown. The process 400 of the method for recognizing a human face includes the following steps:
In this embodiment, an electronic device (e.g., the electronic device shown in fig. 1) on which the method for recognizing a human face is executed may first acquire an image of a user to be authenticated.
In this embodiment, the electronic device may determine an image group in which a similarity between a face object included in a registered image and a face object included in a late-added image in the at least one image group is greater than a third preset threshold. Some of the at least one group of images may comprise a registration image and a late addition image,
the third preset threshold may be set according to actual needs, and the similarity between the face object included in the registered image and the face object included in the later-added image is lower than the third preset threshold, which indicates that the difference between the facial feature of the registered image of the user and the recent facial feature of the registered image of the user is large, and may be caused by reasons such as image processing performed on the registered image of the user. This may also result in the recent facial features of another user being closer to those of the user's registered image, and false identification may occur during authentication. For example, an image of the user a collected during authentication of the user a is very close to a registered image in the group of images associated with the user B, which may cause the face recognition system to recognize the user a as the user B by mistake.
And step 403, querying a post-join image in which the similarity between the face object contained in the determined image group and the face object contained in the acquired image exceeds a second preset threshold.
In this embodiment, the electronic device may query a late-added image, in which the similarity between the face object included in the image group determined in step 402 and the face object included in the image acquired in step 401 exceeds a second preset threshold. In this step, for the image group in which the difference between the features of the face object included in the registered image and the features of the face object included in the post-added image is large, the similarity is calculated only according to the post-added image included in the image group to perform the identity authentication, so that the situation that the face recognition system mistakenly recognizes the user a as the user B due to the fact that the image of the user a acquired during the identity authentication of the user a is close to the registered image in the image group associated with the user B can be avoided.
In this embodiment, in response to querying the late-join image in which the similarity between the face object included in the determined image group and the face object included in the acquired image exceeds the second preset threshold in step 403, the electronic device may determine the image group in which the queried image is located as the target image group, and determine whether the quality data of the acquired image matches the preset data range.
In this embodiment, the electronic device may add the acquired image to the target image group in response to determining that the quality data matches the preset data range in step 404.
In this embodiment, the operations of step 401, step 404, and step 405 are substantially the same as the operations of step 201, step 203, and step 204, and are not described herein again.
As can be seen from fig. 4, compared with the embodiment corresponding to fig. 2, in the flow 400 of the method for recognizing a human face in this embodiment, an image on which the human face is recognized is defined according to the similarity between a human face object included in a registered image in an image group and a human face object included in a post-added image, so that it is possible to avoid recognizing one user as another user, and thus, the scheme described in this embodiment further improves the efficiency of human face recognition.
With further reference to fig. 5, as an implementation of the methods shown in the above-mentioned figures, the present application provides an embodiment of an apparatus for recognizing a human face, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 5, the apparatus 500 for recognizing a human face of the present embodiment includes: an acquisition unit 501, a query unit 502, a determination unit 503, and an addition unit 504. The acquiring unit 501 is configured to acquire an image of a user to be authenticated; an inquiring unit 502, configured to inquire an image in which a similarity between a face object included in at least one image group and a face object included in the acquired image exceeds a preset threshold; a determining unit 503, configured to determine an image group in which the queried image is located as a target image group, and determine whether quality data of the acquired image matches a preset data range; an adding unit 504 for adding the acquired image to the target image group in response to determining that the quality data matches a preset data range.
In this embodiment, the specific processing of the acquiring unit 501, the querying unit 502, the determining unit 503 and the adding unit 504 of the apparatus 500 for recognizing a human face may refer to step 201, step 202, step 203 and step 204 in the corresponding embodiment of fig. 2.
In some optional implementations of this embodiment, the target image group includes a registered image and a late joining image; and a querying unit 502, further configured to: and inquiring a registered image of which the similarity between the face object contained in at least one image group and the face object contained in the acquired image exceeds a first preset threshold, or inquiring a post-added image of which the similarity between the face object contained in at least one image group and the face object contained in the acquired image exceeds a second preset threshold.
In some optional implementations of this embodiment, the second preset threshold is greater than the first preset threshold.
In some optional implementations of this embodiment, the querying unit 502 includes: a determining subunit (not shown in the figure) configured to determine an image group in which a similarity between a face object included in a registered image and a face object included in a later-added image in the at least one image group is greater than a third preset threshold; and an inquiring subunit (not shown in the figure) for inquiring the post-joining image, of which the similarity between the face object contained in the determined image group and the face object contained in the acquired image exceeds a second preset threshold value.
In some optional implementations of this embodiment, the determining unit 503 is further configured to: it is determined whether the brightness and sharpness of the acquired image exceed a fourth preset threshold.
The device provided by the above embodiment of the present application obtains the image of the user to be authenticated; inquiring images of which the similarity between the face object contained in at least one image group and the face object contained in the acquired images exceeds a preset threshold; determining an image group where the inquired image is located as a target image group, and determining whether the quality data of the acquired image is matched with a preset data range; and adding the acquired image into the target image group in response to the determination that the quality data is matched with the preset data range, thereby improving the efficiency of face recognition.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing the electronic device of an embodiment of the present application. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. A driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 601. It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the C language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. 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 involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, a query unit, a determination unit, and an addition unit. The names of these units do not in some cases constitute a limitation on the unit itself, and for example, the acquisition unit may also be described as a "unit for acquiring an image of a user to be authenticated".
As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: acquiring an image of a user to be subjected to identity authentication; inquiring images of which the similarity between the face object contained in at least one image group and the face object contained in the acquired images exceeds a preset threshold; determining an image group where the inquired image is located as a target image group, and determining whether the quality data of the acquired image is matched with a preset data range; in response to determining that the quality data matches the preset data range, adding the acquired image to the target image group.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. A face recognition method, comprising:
acquiring an image of a user to be subjected to identity authentication;
inquiring images of which the similarity between a face object contained in at least one image group and a face object contained in an acquired image exceeds a preset threshold, wherein each image group in the at least one image group is associated with a user identifier in a face recognition system, each image group is created by a gate for carrying out identity verification on each user for the first time, and the face recognition system of the gate is created after the received image of the user, the received image of the user is a registered image, and the image which belongs to the user except the registered image in the image group corresponding to the user is a post-join image;
determining an image group where the inquired image is located as a target image group, and determining whether the quality data of the acquired image is matched with a preset data range;
adding the acquired image into a target image group in response to determining that the quality data is matched with a preset data range;
the querying of the image in which the similarity between the face object contained in the at least one image group and the face object contained in the acquired image exceeds a preset threshold includes:
determining an image group of which the similarity between a face object contained in a registered image and a face object contained in a later-added image is lower than a third preset threshold value in at least one image group;
and querying the post-added image of which the similarity between the face object contained in the determined image group and the face object contained in the acquired image exceeds a second preset threshold.
2. The method of claim 1, wherein the target group of images includes a registration image and a late joining image; and
the querying of the image in which the similarity between the face object contained in the at least one image group and the face object contained in the acquired image exceeds a preset threshold includes:
and inquiring a registered image of which the similarity between the face object contained in at least one image group and the face object contained in the acquired image exceeds a first preset threshold, or inquiring a post-added image of which the similarity between the face object contained in at least one image group and the face object contained in the acquired image exceeds a second preset threshold.
3. The method of claim 2, wherein the second preset threshold is greater than the first preset threshold.
4. The method according to any one of claims 1-3, wherein the determining whether the quality data of the acquired image matches a preset data range comprises:
it is determined whether the brightness and sharpness of the acquired image exceed a fourth preset threshold.
5. A face recognition apparatus comprising:
the system comprises an acquisition unit, a verification unit and a verification unit, wherein the acquisition unit is used for acquiring an image of a user to be subjected to identity verification;
the system comprises an inquiry unit, a registration unit and a processing unit, wherein the inquiry unit is used for inquiring images, the similarity between a face object contained in at least one image group and the face object contained in an acquired image exceeds a preset threshold, each image group in the at least one image group is associated with a user identifier in a face recognition system, each image group is established by a gate for carrying out identity authentication on each user for the first time, the face recognition system of the gate is established after the received image of the user, the received image of the user is a registration image, and the image, except the registration image, belonging to the user in the image group corresponding to the user is a post-join image;
the determining unit is used for determining an image group where the inquired image is located as a target image group and determining whether the quality data of the acquired image is matched with a preset data range;
an adding unit, configured to add the acquired image to the target image group in response to determining that the quality data matches a preset data range;
the query unit comprises:
the determining subunit is used for determining an image group of which the similarity between the face object contained in the registered image and the face object contained in the later-added image is lower than a third preset threshold value;
and the query subunit is used for querying the post-added image of which the similarity between the face object contained in the determined image group and the face object contained in the acquired image exceeds a second preset threshold.
6. The apparatus of claim 5, wherein the target image group comprises a registration image and a late join image; and
the querying element is further configured to:
and inquiring a registered image of which the similarity between the face object contained in at least one image group and the face object contained in the acquired image exceeds a first preset threshold, or inquiring a post-added image of which the similarity between the face object contained in at least one image group and the face object contained in the acquired image exceeds a second preset threshold.
7. The apparatus of claim 6, wherein the second preset threshold is greater than the first preset threshold.
8. The apparatus according to any of claims 5-7, wherein the determining unit is further configured to:
it is determined whether the brightness and sharpness of the acquired image exceed a fourth preset threshold.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method recited in any of claims 1-4.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-4.
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| CN109241931A (en) * | 2018-09-20 | 2019-01-18 | 上海博泰悦臻网络技术服务有限公司 | Automatic navigation method, terminal and system based on recognition of face |
| CN112115740B (en) * | 2019-06-19 | 2024-04-09 | 京东科技信息技术有限公司 | Method and apparatus for processing image |
| CN110825765B (en) * | 2019-10-23 | 2022-10-04 | 中国建设银行股份有限公司 | Face recognition method and device |
| CN111405249A (en) * | 2020-03-20 | 2020-07-10 | 腾讯云计算（北京）有限责任公司 | Monitoring method, monitoring device, server and computer-readable storage medium |
| CN112818885A (en) * | 2021-02-07 | 2021-05-18 | 中国银联股份有限公司 | Face recognition method, device, equipment and storage medium |
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