CN111325078A - Face recognition method, face recognition device and storage medium - Google Patents
Face recognition method, face recognition device and storage medium Download PDFInfo
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- CN111325078A CN111325078A CN201811544634.8A CN201811544634A CN111325078A CN 111325078 A CN111325078 A CN 111325078A CN 201811544634 A CN201811544634 A CN 201811544634A CN 111325078 A CN111325078 A CN 111325078A
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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
- G06V40/161—Detection; Localisation; Normalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
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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
- G06V40/168—Feature extraction; Face representation
Abstract
The application discloses a face recognition method, a face recognition device and a storage medium. The method relates to the field of image recognition, and is used for solving the problems that the accuracy is not high and the success rate of matching results is not high when face recognition is carried out in places with large personnel mobility. The method comprises the following steps: acquiring an image in a shooting range through a wide-angle high-definition camera; acquiring three-dimensional face shape data of each face image in the image; carrying out face feature extraction and modeling on the face three-dimensional shape data of each face image to obtain each face feature data; matching each face characteristic data with pre-stored characteristic template data; and sending the successfully matched human face feature data to an upper computer terminal. Therefore, the human face feature data is matched with the pre-stored feature template data, so that the identification accuracy is improved, and the accuracy of the matching result is higher.
Description
Technical Field
The present invention relates to the field of image recognition, and in particular, to a face recognition method, apparatus, and storage medium.
Background
With the development of social informatization, the use of face recognition technology is becoming more and more common, and especially in places with large flow of people or some other related places, the demand for face recognition is also increasing, for example: in places with large personnel mobility, such as airports, superstores and some event places, people need to be quickly identified. In the prior art, the face recognition is carried out in a place with large personnel mobility, so that the accuracy is not high, and the success rate of the matching result is not high.
Disclosure of Invention
The embodiment of the application provides a face recognition method, a face recognition device and a storage medium. The face three-dimensional shape data of the face image to be recognized is obtained, the face features are extracted and the model is built, so that the recognition accuracy can be improved, and the accuracy of the matching result is higher.
In a first aspect, an embodiment of the present application provides a face recognition method, where the method includes:
acquiring an image in a shooting range through a wide-angle high-definition camera;
acquiring three-dimensional face shape data of each face image in the image;
carrying out face feature extraction and modeling on the face three-dimensional shape data of each face image to obtain each face feature data;
matching each face characteristic data with pre-stored characteristic template data;
and sending the successfully matched human face feature data to an upper computer terminal.
In a second aspect, an embodiment of the present application provides a face recognition apparatus, including:
the image acquisition module is used for acquiring images in a shooting range through the wide-angle high-definition camera;
the data acquisition module is used for acquiring the three-dimensional face shape data of each face image in the image;
the extraction modeling module is used for extracting the face characteristics of the three-dimensional face shape data of each face image and modeling to obtain the face characteristic data;
the matching module is used for matching the face characteristic data with the pre-stored characteristic template data;
and the sending module is used for sending the successfully matched human face feature data to the upper computer terminal.
In a third aspect, another embodiment of the present application further provides a computing device comprising at least one processor; and;
a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute a face recognition method provided by the embodiment of the application.
In a fourth aspect, another embodiment of the present application further provides a computer storage medium, where the computer storage medium stores computer-executable instructions, and the computer-executable instructions are configured to cause a computer to execute a face recognition method in an embodiment of the present application.
The embodiment of the application provides a face recognition method, a face recognition device and a storage medium, wherein face three-dimensional shape data of a face image to be recognized is obtained, face features of the face image are extracted, and modeling is carried out to obtain face feature data; the human face feature data are matched with the feature template data stored in advance, so that the identification accuracy can be improved, and the accuracy of a matching result is higher.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart of a face recognition method in an embodiment of the present application;
FIG. 2 is a flow chart of a face recognition method in an embodiment of the present application;
FIG. 3 is a schematic diagram of a face recognition structure in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computing device according to an embodiment of the present application.
Detailed Description
In order to improve the accuracy of face recognition and further improve the accuracy of a matching result in an environment with large personnel mobility, the embodiment of the application provides a face recognition method, a face recognition device and a storage medium. In order to better understand the technical solution provided by the embodiments of the present application, the following brief description is made on the basic principle of the solution:
the method comprises the steps of obtaining an image containing a plurality of face images, and obtaining face three-dimensional shape data of each face image in the image. And extracting and modeling the obtained three-dimensional face shape data to obtain the characteristic data of each face. The face features and the data in the sample library are used for matching and identifying, so that the accuracy of face identification can be improved, and the accuracy of a matching result is higher.
The face recognition method is further explained by the specific embodiment. Fig. 1 is a schematic flow chart of a face recognition method, which includes the following steps:
step 101: and acquiring an image in a shooting range through the wide-angle high-definition camera.
The wide-angle high-definition camera can acquire images in a wider range.
Step 102: and acquiring the three-dimensional face shape data of each face image in the image.
Step 103: and carrying out face feature extraction and modeling on the face three-dimensional shape data of each face image to obtain each face feature data.
Step 104: and matching the characteristic data of each human face with the characteristic template data stored in advance.
Step 105: and sending the successfully matched human face feature data to an upper computer terminal.
Therefore, under the environment of large personnel flow, the accuracy of face recognition can be improved by adopting the face three-dimensional shape data of the face image, and the face feature data obtained by extracting the face features and modeling according to the face three-dimensional shape data is matched with the feature template data in the sample library, so that the accuracy of the matching result is higher.
In the embodiment of the present application, in order to improve the recognition rate of face recognition, a series of image quality improvements performed on an acquired face image by an image processing technology may specifically be implemented as follows: and carrying out image preprocessing on the image to obtain a preprocessed image.
Therefore, the step 102 specifically operates as follows: and acquiring the three-dimensional face shape data of each face image in the preprocessed image.
Therefore, the obtained image is preprocessed and subjected to image enhancement, and the recognition rate of face recognition can be improved.
In the above, how to process the acquired image to make the recognition rate of the image higher is introduced, and the matching of the extracted face feature data and the sample library is further described below.
In the embodiment of the present application, whether matching is successful is determined by matching similarity between the face feature data and each feature template data stored in advance, which may be specifically implemented as steps a 1-A3:
step A1: and executing the matching similarity of the acquired face feature data and the pre-stored feature template data aiming at the face feature data.
Step A2: and judging whether the acquired matching similarity is greater than a preset threshold value.
Step A3: and if the matching similarity of the face features and at least one feature template data is greater than a preset threshold value, determining that the face feature data is successfully matched.
Thus, the matching result can be more accurate by comparing the acquired matching similarity with the preset threshold.
In one embodiment, if the matching similarity between one face feature and the plurality of feature template data is greater than a preset threshold, the feature template data with the maximum matching similarity value is used as the matching result.
In one embodiment, if the matching similarity between a plurality of facial features and one feature template data in the feature template data is greater than a preset threshold, the facial feature with the maximum matching similarity value is taken as the matching result.
In this embodiment of the present application, if the matching similarity between one face feature and each feature template data is not greater than the preset threshold, the present application further includes: and storing the face feature data as new face feature data.
Therefore, the sample library can be expanded by storing the face feature data, so that the subsequent recognition and matching results are more accurate.
In this embodiment of the application, in order to avoid or reduce missing of face recognition, a plurality of images may be captured continuously for recognition, which may be specifically implemented as: and continuously snapping through a wide-angle high-definition camera to acquire a plurality of images within a shooting range.
Thus, before step 104, it is also necessary to detect a plurality of pieces of acquired face feature data, and duplicate the same face feature data. Therefore, omission of face recognition can be avoided or reduced by continuously capturing a plurality of images, the same face characteristic data is rearranged, redundant face characteristic data can be removed, and the time for face recognition matching is saved.
In the embodiment of the application, the successfully matched face characteristic data is sent to the upper computer terminal, and the upper computer terminal records the face information corresponding to the face characteristic data and prompts related personnel to take measures.
In one embodiment, if the matched face features are images of criminal suspects, the images can be sent to an upper computer terminal to remind police personnel to implement capturing actions.
The processes of the various parts of the present application are introduced above, and the overall process of the embodiment of the present application is described in detail below, as shown in fig. 2.
Step 201: and acquiring an image within the shooting range.
When the method is specifically implemented, the images in the shooting range can be acquired through the wide-angle high-definition camera. Therefore, the wide-angle high-definition camera can acquire images in a wider range, and the acquired information amount is large.
Step 202: and carrying out image preprocessing on the image.
In specific implementation, a series of image quality improvement can be performed on the acquired images through an image processing technology, so that the acquired face images are clearer and more accurate.
Step 203: and acquiring the three-dimensional face shape data of each face image in the preprocessed image.
Step 204: and carrying out face feature extraction and modeling on the face three-dimensional shape data of each face image to obtain each face feature data.
In specific implementation, the face feature extraction and modeling can be performed on the acquired face three-dimensional shape data of each face image, so that face feature data corresponding to the face three-dimensional shape data of each face image is obtained.
Step 205: and carrying out duplicate removal on the same face feature data.
In specific implementation, multiple pieces of acquired human face feature data can be detected, and the same human face feature data is subjected to repetition elimination. Therefore, redundant face characteristic data can be removed, and the time for face recognition and matching is saved.
Step 206: and acquiring the matching similarity of the face feature data and each pre-stored feature template data.
In specific implementation, each face feature data after the rearrangement can be matched with the feature template data in the sample library to obtain matching similarity.
Step 207: and judging whether the acquired matching similarity is greater than a preset threshold value. If yes, go to step 208; if not, go to step 209.
Step 208: and sending the data to an upper computer terminal.
When the method is specifically implemented, the successfully matched face characteristic data can be sent to the upper computer terminal, the face information corresponding to the face characteristic data is recorded through the upper computer terminal, and relevant personnel are prompted to take measures.
Step 209: and storing the face feature data as new face feature data.
In specific implementation, the face feature data can be stored as newly added face feature data; therefore, the sample library can be expanded by storing the face feature data, so that the subsequent recognition and matching results are more accurate.
Based on the same inventive concept, an embodiment of the present application further provides a face recognition apparatus, as shown in fig. 3, which is a schematic structural diagram of the apparatus, including:
an image acquisition module 301, configured to acquire an image within a shooting range through a wide-angle high-definition camera;
an obtaining data module 302, configured to obtain face three-dimensional shape data of each face image in the image;
the extraction modeling module 303 is configured to perform face feature extraction on the three-dimensional face shape data of each face image and perform modeling to obtain each face feature data;
a matching module 304, configured to match each of the face feature data with pre-stored feature template data;
and the sending module 305 is used for sending the successfully matched human face feature data to the upper computer terminal.
Further, the apparatus further comprises:
the preprocessing module is configured to perform image preprocessing on the image before the data obtaining module 302 obtains the three-dimensional face shape data of each face image in the image, so as to obtain a preprocessed image;
an acquire data module 302, comprising:
and the data acquisition unit is used for acquiring the three-dimensional face shape data of each face image in the preprocessed image.
Further, the apparatus further comprises:
an acquiring similarity module, configured to perform, after the matching module 304 matches each face feature data with pre-stored feature template data, acquiring, for each face feature data, a matching similarity between the face feature data and each pre-stored feature template data;
the judging module is used for judging whether the acquired matching similarity is greater than a preset threshold value or not;
and the matching success module is used for determining that the face feature data is successfully matched if the matching similarity of the face feature and at least one feature template data is greater than a preset threshold.
Further, the apparatus further comprises:
and the storage module is used for storing the face feature data as the newly added face feature data if the matching similarity between the face feature and each feature template data is not greater than the preset threshold after the judgment module judges whether the acquired matching similarity is greater than the preset threshold.
Further, continuously capturing through a wide-angle high-definition camera to obtain a plurality of images within a shooting range; the device further comprises:
and the duplication elimination module is used for eliminating duplication of the same face feature data before the matching module 304 matches each face feature data with the pre-stored feature template data.
Having described the method and apparatus for face recognition according to an exemplary embodiment of the present application, a computing apparatus according to another exemplary embodiment of the present application is described next.
As will be appreciated by one skilled in the art, aspects of the present application may be embodied as a system, method or program product. Accordingly, various aspects of the present application may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
In some possible implementations, a computing device may include at least one processor, and at least one memory, according to embodiments of the application. Wherein the memory stores program code, which when executed by the processor, causes the processor to execute the steps 101-105 of the face recognition method according to various exemplary embodiments of the present application described above in the present specification.
The computing device 40 according to this embodiment of the present application is described below with reference to fig. 4. The computing device 40 shown in fig. 4 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present application. The computing device may be, for example, a cell phone, a tablet computer, or the like.
As shown in fig. 4, computing device 40 is embodied in the form of a general purpose computing device. Components of computing device 40 may include, but are not limited to: the at least one processor 41, the at least one memory 42, and a bus 43 connecting the various system components (including the memory 42 and the processor 41).
The memory 42 may include readable media in the form of volatile memory, such as Random Access Memory (RAM)421 and/or cache memory 422, and may further include Read Only Memory (ROM) 423.
In some possible embodiments, the aspects of the face recognition method provided in the present application may also be implemented in the form of a program product, which includes program code for causing a computer device to execute the steps in the method for face recognition according to various exemplary embodiments of the present application described above in this specification, when the program product runs on the computer device, execute the steps 101 and 105 as shown in fig. 1.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A 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 (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, 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.
The face recognition method of the embodiment of the application can adopt a portable compact disc read only memory (CD-ROM) and comprises program codes, and can be operated on a computing device. However, the program product of the present application is not limited thereto, and in this document, a 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.
A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a 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 readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations 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, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user computing device, partly on the user equipment, as a stand-alone software package, partly on the user computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functions of two or more units described above may be embodied in one unit, according to embodiments of the application. Conversely, the features and functions of one unit described above may be further divided into embodiments by a plurality of units.
Moreover, although the operations of the methods of the present application are depicted in the drawings in a sequential order, this does not require or imply that these operations must be performed in this order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a manner that causes the instructions stored in the computer-readable memory to produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (12)
1. A face recognition method, comprising:
acquiring an image in a shooting range through a wide-angle high-definition camera;
acquiring three-dimensional face shape data of each face image in the image;
carrying out face feature extraction and modeling on the face three-dimensional shape data of each face image to obtain each face feature data;
matching each face characteristic data with pre-stored characteristic template data;
and sending the successfully matched human face feature data to an upper computer terminal.
2. The method of claim 1, wherein prior to obtaining the three-dimensional shape data of the face of each of the face images in the image, the method further comprises:
carrying out image preprocessing on the image to obtain a preprocessed image;
the acquiring of the three-dimensional face shape data of each face image in the image specifically includes:
and acquiring the three-dimensional face shape data of each face image in the preprocessed image.
3. The method according to claim 1, wherein after matching each of the face feature data with pre-stored feature template data, the method further comprises:
executing the following steps aiming at each face feature data:
acquiring the matching similarity of the face feature data and each pre-stored feature template data;
judging whether the acquired matching similarity is greater than a preset threshold value or not;
if the matching similarity between the human face feature and at least one feature template data is greater than a preset threshold value,
the face feature data is determined to be successfully matched.
4. The method according to claim 3, wherein after determining whether each obtained matching similarity is greater than a preset threshold, the method further comprises:
and if the matching similarity of the face features and the feature template data is not greater than a preset threshold, storing the face feature data as newly added face feature data.
5. The method according to claim 1, characterized in that a plurality of images within a shooting range are acquired by continuous capturing through a wide-angle high-definition camera;
before the matching of each face feature data with the pre-stored feature template data, the method further includes:
and carrying out duplicate removal on the same face feature data.
6. An apparatus for face recognition, the apparatus comprising:
the image acquisition module is used for acquiring images in a shooting range through the wide-angle high-definition camera;
the data acquisition module is used for acquiring the three-dimensional face shape data of each face image in the image;
the extraction modeling module is used for extracting the face characteristics of the three-dimensional face shape data of each face image and modeling to obtain the face characteristic data;
the matching module is used for matching the face characteristic data with the pre-stored characteristic template data;
and the sending module is used for sending the successfully matched human face feature data to the upper computer terminal.
7. The apparatus of claim 6, wherein before the obtaining data module obtains the three-dimensional shape data of the face of each of the face images in the image, the apparatus further comprises:
the preprocessing module is used for preprocessing the image to obtain a preprocessed image;
a data acquisition module comprising:
and the data acquisition unit is used for acquiring the three-dimensional face shape data of each face image in the preprocessed image.
8. The apparatus according to claim 6, wherein after the matching module matches each of the face feature data with the pre-stored feature template data, the apparatus further comprises:
the acquisition similarity module is used for acquiring the matching similarity of the face feature data and each pre-stored feature template data aiming at each face feature data;
the judging module is used for judging whether the acquired matching similarity is greater than a preset threshold value or not;
and the matching success module is used for determining that the face feature data is successfully matched if the matching similarity of the face feature and at least one feature template data is greater than a preset threshold.
9. The apparatus according to claim 8, wherein after the determining module determines whether the obtained matching similarity is greater than a preset threshold, the apparatus further comprises:
and the storage module is used for storing the face feature data as the newly added face feature data if the matching similarity between the face feature and each feature template data is not greater than a preset threshold.
10. The device of claim 6, wherein a plurality of images within a shooting range are acquired by continuous capturing through a wide-angle high-definition camera;
before the matching module matches each face feature data with the pre-stored feature template data, the device further comprises:
and the duplication removing module is used for carrying out duplication removal on the same face feature data.
11. A computer-readable medium having stored thereon computer-executable instructions for performing the method of any one of claims 1-5.
12. A computing device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
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| CN114429663A (en) * | 2022-01-28 | 2022-05-03 | 北京百度网讯科技有限公司 | Updating method of human face base, human face recognition method, device and system |
| CN114429663B (en) * | 2022-01-28 | 2023-10-20 | 北京百度网讯科技有限公司 | Updating method of face base, face recognition method, device and system |
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