CN110210290A - Face picture acquisition method, device and computer equipment - Google Patents

Abstract

The embodiment of the present application provides a face picture collection method, device and computer equipment, wherein the method includes firstly collecting a plurality of face sample pictures of a plurality of users through a plurality of image acquisition devices with different acquisition parameters, and obtaining each The face sample picture set corresponding to each image acquisition device; secondly, the face sample pictures are extracted from the face sample picture sets corresponding to each image acquisition device according to the preset ratio, and the face sample pictures are formed based on the extracted face sample pictures. A collection of face pictures; finally, compare the obtained picture to be recognized with the face sample pictures in the collection of face pictures to obtain a face sample picture matching the picture to be recognized. The face pictures collected in this way are more in line with the changes of the actual scene, and have a wider range of applications in the field of artificial intelligence, especially in biometrics.

Description

Face picture collection method, device and computer equipment

technical field

The present application relates to the technical field of artificial intelligence, and in particular to a face picture collection method, device and computer equipment.

Background technique

In the existing performance testing process of face pictures, for different picture collection devices, there is often a problem that the resolution, pixel and format of the pictures are not uniform.

However, in practical applications, the face image data uses a fixed image, but the image acquisition device used to collect the image is random, so for the collected face image, there will be cases where no corresponding image can be found. Issues with the resolution, pixels, or format of the baseline image prevent subsequent testing.

Contents of the invention

The embodiment of the present application provides a face picture collection method, device and computer equipment, which can compare the face sample pictures with the acquired pictures to be recognized, and obtain the face sample pictures with the same collection parameters, which is more in line with Changes in actual scenarios have a wider range of applications.

In the first aspect, the embodiment of the present application provides a method for collecting face pictures, including:

Collect multiple face sample pictures of multiple users through multiple image acquisition devices with different acquisition parameters, and obtain a face sample picture set corresponding to each image acquisition device;

Extract the face sample pictures from the face sample picture sets corresponding to each image acquisition device according to the preset ratio, and form a face picture collection based on the extracted face sample pictures;

Comparing the obtained picture to be recognized with the face sample picture in the collection of face pictures to obtain a face sample picture matching the picture to be recognized.

Wherein, in a possible implementation manner, before collecting multiple face sample pictures of multiple users through multiple image acquisition devices with different acquisition parameters, it also includes:

Configure the acquisition parameters for each image acquisition device.

Wherein in a possible implementation manner, said collecting multiple face sample pictures of multiple users respectively through multiple image acquisition devices with different acquisition parameters includes:

Each image acquisition device captures the faces of multiple users multiple times to obtain a face sample picture set corresponding to each image acquisition device.

Wherein, in a possible implementation manner, the plurality of image acquisition devices with different acquisition parameters are used to collect a plurality of face sample pictures of multiple users respectively, and obtain a set of face sample pictures corresponding to each image acquisition device ,Also includes:

Detecting each face sample picture, and comparing the quality of each face sample picture with a preset picture quality, and,

When the quality of the human face sample picture is not lower than the preset picture quality, output the human face sample picture to a corresponding human face sample picture set.

In the second aspect, the embodiment of the present application also provides a device for collecting face pictures, including:

The collection module is used to collect a plurality of face sample pictures of multiple users through a plurality of image collection devices with different collection parameters, and obtain a set of face sample pictures corresponding to each image collection device;

The extraction module is used to extract face sample pictures from the set of face sample pictures corresponding to each image acquisition device collected by the acquisition module according to a preset ratio, and form a face picture based on the extracted face sample pictures Collection;

A comparison module, configured to compare the obtained picture to be identified with the face sample picture in the collection of human face pictures extracted by the extraction module, to obtain a face sample picture that matches the picture to be identified .

Wherein in a possible implementation manner, the device further includes:

The configuration module is used to configure the acquisition parameters of each image acquisition device.

In a possible implementation manner, the acquisition module is specifically configured to capture multiple times of faces of multiple users through each image acquisition device, so as to obtain a face sample picture set corresponding to each image acquisition device.

Wherein in a possible implementation manner, the device further includes:

A detection module, configured to detect each face sample picture collected by the acquisition module, and compare the quality of each face sample picture with a preset picture quality, and,

When the quality of the human face sample picture is not lower than the preset picture quality, output the human face sample picture to a corresponding human face sample picture set.

In the third aspect, the embodiment of the present application also provides a computer device, including a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor executes the computer program , realize the above-mentioned face picture collection method.

In a fourth aspect, the embodiment of the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned face picture collection method is implemented.

In the above technical solution, a plurality of face sample pictures of multiple users are respectively collected by a plurality of image acquisition devices with different acquisition parameters, and after obtaining the face sample picture sets corresponding to each image acquisition device, they are divided according to preset ratios. Extract face sample pictures from each face sample picture set, and form a face picture collection based on the extracted face sample pictures, and perform face sample pictures on the acquired pictures to be recognized based on the face picture collection The comparison, and obtain the corresponding face sample pictures, which is more in line with the changes of the actual scene, and has a wider range of applications.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description These are some embodiments of the present application. Those skilled in the art can also obtain other drawings based on these drawings without any creative effort.

Fig. 1 is the flowchart of an embodiment of the applicant's face picture collection method;

Fig. 2 is the flowchart of another embodiment of the applicant's face picture collection method;

Fig. 3 is a schematic diagram of the connection structure of an embodiment of the applicant's face picture collection device;

Fig. 4 is a schematic diagram of the connection structure of another embodiment of the applicant's face image collection device;

FIG. 5 is a schematic structural diagram of an embodiment of a computer device of the present application.

Detailed ways

In order to better understand the technical solutions of the present application, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings.

It should be clear that the described embodiments are only some of the embodiments of the present application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this application.

Terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of this application and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

Fig. 1 is the flow chart of an embodiment of the applicant's face picture collection method, as shown in Fig. 1, said method may comprise:

Step 101: Collect multiple face sample pictures of multiple users through multiple image capture devices with different capture parameters, and obtain a set of face sample pictures corresponding to each image capture device.

Specifically, the above-mentioned image acquisition device can be any camera that uses electronic sensors to convert optical images into electronic data, including but not limited to SLR cameras, mirrorless cameras, etc. In practical applications, each image is often captured by snapping A set of face sample pictures corresponding to the acquisition device. Wherein, the image acquisition device in this embodiment has different acquisition parameters, and the above acquisition parameters generally refer to the resolution, pixels, and format of the picture. Preferably, in this embodiment, the acquisition parameters of the above-mentioned image acquisition equipment can be configured in advance, wherein the resolution is generally configured to be greater than or equal to 72 pixels per inch, the pixels are generally greater than or equal to 2 million, and the formats are generally .jpg, .png, .bmp etc.

Step 102: Extract face sample pictures from the face sample picture sets corresponding to each image acquisition device according to a preset ratio, and form a face picture collection based on the extracted face sample pictures.

Specifically, the above-mentioned preset ratio can be set according to system performance and/or implementation requirements during actual implementation. This embodiment does not limit the size of the above-mentioned preset ratio. For example, the above-mentioned preset ratio can be 10%.

Step 103: Compare the obtained picture to be recognized with the face sample picture in the above-mentioned face picture collection, and obtain a face sample picture matching the above-mentioned picture to be recognized.

Specifically, the application compares the resolution, pixels, and format of the picture to be recognized with the face sample pictures with different collection parameters to obtain the corresponding face sample pictures. The above comparison process is described below by taking the resolution as an example:

First of all, we need to obtain the resolution of the picture to be recognized, which can generally be viewed by right-clicking on a computer application and selecting properties; secondly, we need to find the resolution that is closest to the picture to be recognized in the face picture collection. It is a consistent face sample picture; finally, through the found face sample picture, it can be used by testers for further operations according to actual test requirements.

In practical applications, since there are often multiple face sample pictures found above, the identity information of the person can be determined by comparing the similarity between the above-mentioned picture to be recognized and the face sample picture. Specifically, the above similarity The degree comparison can often be done by using some similarity algorithms, including but not limited to scale-invariant feature transform (Scale-invariant feature transform; SIFT for short) algorithm or hash algorithm. In this embodiment, the cosine similarity algorithm is used to calculate the similarity between the picture to be recognized and the face sample picture. Specifically, this embodiment measures the vector inner product between the picture to be recognized and a plurality of face sample pictures space to measure the similarity between them. Example:

Generally, the cosine similarity of the feature vector between the picture to be recognized and the face sample picture is calculated by formula (1):

Among them, the formula (1) in Indicates the feature vector corresponding to the picture to be recognized, Indicates the feature vector corresponding to the face sample picture.

Among them, the closer the value of cosθ is to 1, the higher the similarity between the picture to be recognized and the face sample picture is. In this way, the identity of the person in the picture to be recognized can be determined according to the identity information recorded in the face sample picture.

In the above-mentioned face picture collection method, a plurality of face sample pictures of multiple users are respectively collected by a plurality of image collection devices with different collection parameters, and after obtaining the face sample picture set corresponding to each image collection device, according to preset Set the ratio to extract face sample pictures from each face sample picture set, and form a face picture collection based on the extracted face sample pictures, and based on the face picture collection, perform human face recognition on the acquired pictures to be recognized. In the comparison of face sample pictures, the corresponding face sample pictures can be obtained during the comparison process. At the same time, the face picture collection can be applied to the test requirements of various face sample pictures, which is more in line with the actual scene changes. Has a wider range of applications.

Fig. 2 is the flow chart of another embodiment of the applicant's face picture collection method, as shown in Fig. 2, in the embodiment shown in Fig. 1 of the present application, step 101 may include:

Step 201: Detect each face sample picture, and compare the quality of each face sample picture with a preset picture quality.

Step 202: When the quality of the face sample picture is not lower than the preset picture quality, output the face sample picture to the corresponding face sample picture set.

Furthermore, the meaning of picture quality mainly includes two aspects, that is, the fidelity of the image and the intelligibility of the image. Image quality directly depends on the optical performance of imaging equipment, image contrast, instrument noise and other factors. Through quality evaluation, monitoring methods can be provided for image acquisition and processing.

Similarly, the above-mentioned preset picture quality can be set according to system performance and/or implementation requirements during specific implementation. This embodiment does not limit the above-mentioned preset picture quality (fidelity and intelligibility), for example For example, the above-mentioned fidelity and intelligibility can be 90%, and when one of the fidelity and intelligibility is lower than 90%, it is considered that the quality of the face sample picture is lower than the preset picture quality .

Fig. 3 is a schematic diagram of the connection structure of an embodiment of the applicant's face picture collection device. As shown in Fig. 3, the above-mentioned device may include:

The collection module 31 is configured to collect a plurality of face sample pictures of multiple users through a plurality of image collection devices with different collection parameters, and obtain a set of face sample pictures corresponding to each image collection device.

Specifically, the above-mentioned image acquisition device can be any camera that uses electronic sensors to convert optical images into electronic data, including but not limited to SLR cameras, mirrorless cameras, etc. In practical applications, each image is often captured by snapping A set of face sample pictures corresponding to the acquisition device. Wherein, the image acquisition device in this embodiment has different acquisition parameters, and the above acquisition parameters generally refer to the resolution, pixels, and format of the picture. Preferably, the device in this embodiment further includes a configuration module 30, which is used to configure the acquisition parameters of the above-mentioned image acquisition device in advance, wherein the resolution is generally configured to be greater than or equal to 72 pixels per inch, and the pixel is generally greater than or It is equal to 2 million, and the formats generally include .jpg, .png, .bmp, etc.

The extraction module 32 is used to extract the face sample pictures from the face sample picture collection corresponding to each image acquisition device collected by the above-mentioned acquisition module according to a preset ratio, and form a face sample based on the extracted face sample pictures Picture collection.

Specifically, the above-mentioned preset ratio can be set according to system performance and/or implementation requirements during actual implementation. This embodiment does not limit the size of the above-mentioned preset ratio. For example, the above-mentioned preset ratio can be 10%.

The comparison module 33 is used to compare the obtained picture to be recognized with the face sample picture in the above-mentioned face picture collection extracted by the above-mentioned extraction module, and obtain a face sample picture matching the above-mentioned picture to be recognized.

Specifically, the application compares the resolution, pixels, and format of the picture to be recognized with the face sample pictures with different collection parameters to obtain the corresponding face sample pictures. The above comparison process is described below by taking the resolution as an example:

First of all, this embodiment needs to first obtain the resolution of the picture to be recognized, which can generally be checked by right-clicking on a computer application program to select properties; secondly, this embodiment needs to find the resolution of the picture to be recognized in the face picture collection. The face sample pictures with the closest or even the same rate; finally, the found face sample pictures can be used by testers for further operations according to actual test requirements.

In practical applications, since there are often multiple face sample pictures found above, the identity information of the person can be determined by comparing the similarity between the above-mentioned picture to be recognized and the face sample picture. Specifically, the above similarity The degree comparison can often be done by using some similarity algorithms, including but not limited to scale-invariant feature transform (Scale-invariant feature transform; SIFT for short) algorithm or hash algorithm. In this embodiment, the cosine similarity algorithm is used to calculate the similarity between the picture to be recognized and the face sample picture. Specifically, this embodiment measures the vector inner product between the picture to be recognized and a plurality of face sample pictures space to measure the similarity between them. Example:

Generally, the cosine similarity of the feature vector between the picture to be recognized and the face sample picture is calculated by formula (1):

Among them, the formula (1) in Indicates the feature vector corresponding to the picture to be recognized, Indicates the feature vector corresponding to the face sample picture.

Among them, the closer the value of cosθ is to 1, the higher the similarity between the picture to be recognized and the face sample picture is. In this way, the identity of the person in the picture to be recognized can be determined according to the identity information recorded in the face sample picture.

In the above-mentioned face picture collection method, the collection module 31 respectively collects a plurality of face sample pictures of multiple users through a plurality of image collection devices with different collection parameters, and obtains the face sample picture set corresponding to each image collection device , and then extract the face sample pictures from the face sample picture collection corresponding to each image acquisition device collected by the above-mentioned acquisition module according to the preset ratio by the extraction module 32, and form a face picture based on the extracted human face sample pictures Collection; Finally, the picture to be identified is obtained by the comparison module 33 and the face sample picture in the above-mentioned face picture collection extracted by the above-mentioned extraction module 32 is compared to obtain a face sample matched with the above-mentioned picture to be identified The picture is more in line with the changes of the actual scene and has a wider range of applications.

Fig. 4 is a schematic diagram of the connection structure of another embodiment of the applicant's face picture collection device. As shown in Fig. 4, the above-mentioned device may also include:

The detection module 41 is used to detect each face sample picture collected by the acquisition module, and compare the quality of each face sample picture with the preset picture quality, and,

When the quality of the human face sample picture is not lower than the preset picture quality, output the human face sample picture to the corresponding human face sample picture set.

Furthermore, the meaning of picture quality mainly includes two aspects, that is, the fidelity of the image and the intelligibility of the image. Image quality directly depends on the optical performance of imaging equipment, image contrast, instrument noise and other factors. Through quality evaluation, monitoring methods can be provided for image acquisition and processing.

Similarly, the above-mentioned preset picture quality can be set according to system performance and/or implementation requirements during specific implementation. This embodiment does not limit the above-mentioned preset picture quality (fidelity and intelligibility), for example For example, the above-mentioned degree of fidelity and intelligibility may be 90%, and when one of the degree of fidelity and intelligibility is lower than 90%, it is considered that the quality of the face sample picture is lower than the above-mentioned preset picture quality.

Fig. 5 is a schematic structural diagram of an embodiment of the computer device of the present application. The computer device may include a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the computer program, The face picture collection method provided in the embodiment of the present application can be implemented.

Wherein, the above-mentioned computer equipment may be a server, such as: a cloud server, or the above-mentioned computer equipment may also be an electronic equipment, such as: a smart phone, a smart watch, a personal computer (Personal Computer; hereinafter referred to as: PC), a notebook computer or a tablet computer, etc. As for the smart device, this embodiment does not limit the specific form of the above computer device.

FIG. 5 shows a block diagram of an exemplary computer device 52 suitable for use in implementing embodiments of the present application. The computer device 52 shown in FIG. 5 is only an example, and should not limit the functions and scope of use of this embodiment of the present application.

As shown in FIG. 5, computer device 52 takes the form of a general-purpose computing device. Components of computer device 52 may include, but are not limited to, one or more processors or processing units 56 , system memory 78 , bus 58 connecting various system components including system memory 78 and processing unit 56 .

Bus 58 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (Industry Standard Architecture; hereinafter referred to as: ISA) bus, Micro Channel Architecture (Micro Channel Architecture; hereinafter referred to as: MAC) bus, enhanced ISA bus, video electronics standard Association (Video Electronics Standards Association; hereinafter referred to as: VESA) local bus and peripheral component interconnection (Peripheral Component Interconnection; hereinafter referred to as: PCI) bus.

Computer device 52 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computing device 52 and include both volatile and nonvolatile media, removable and non-removable media.

The system memory 78 may include a computer system readable medium in the form of a volatile memory, such as a random access memory (Random Access Memory; RAM for short) 70 and/or a cache memory 72 . Computer device 52 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 74 may be used to read and write to non-removable, non-volatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard drive"). Although not shown in FIG. 5, a disk drive for reading and writing to a removable nonvolatile disk (such as a "floppy disk") may be provided, as well as a removable nonvolatile disk (such as a Compact Disk ROM (Compact Disk)). Disc Read Only Memory (hereinafter referred to as: CD-ROM), Digital Video Disc Read Only Memory (hereinafter referred to as: DVD-ROM) or other optical media) read and write optical disc drives. In these cases, each drive may be connected to bus 58 through one or more data media interfaces. The memory 78 may include at least one program product having a set (eg, at least one) of program modules configured to perform the functions of the various embodiments of the present application.

A program/utility 80 having a set (at least one) of program modules 82, such as may be stored in memory 78, such as, but not limited to, an operating system, one or more application programs, other program Modules and program data, each or some combination of these examples may include the implementation of the network environment. The program modules 82 generally perform the functions and/or methods of the embodiments described herein.

The computer device 52 may also communicate with one or more external devices 54 (e.g., a keyboard, pointing device, display 64, etc.), and with one or more devices that enable a user to interact with the computer device 52, and/or with Any device (eg, network card, modem, etc.) that enables the computing device 52 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 62 . Moreover, the computer device 52 can also be connected with one or more networks (such as a local area network (Local Area Network; hereinafter referred to as: LAN), a wide area network (Wide Area Network; hereinafter referred to as: WAN) and/or public networks, such as the Internet, through the network adapter 60. ) communication. As shown in FIG. 5 , network adapter 60 communicates with other modules of computer device 52 via bus 58 . It should be appreciated that although not shown in FIG. 5, other hardware and/or software modules may be used in conjunction with computer device 52, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape Drives and data backup storage systems, etc.

The processing unit 56 executes various functional applications and data processing by running the programs stored in the system memory 78 , for example, implementing the face picture collection method provided by the embodiment of the present application.

The embodiment of the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the above computer program is executed by a processor, the face image collection method provided in the embodiment of the present application can be realized.

Any combination of one or more computer-readable media may be used for the above-mentioned non-transitory computer-readable storage medium. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more conductors, portable computer disks, hard disks, Random Access Memory (RAM), Read Only Memory (Read Only Memory) ; Hereinafter referred to as: ROM), Erasable Programmable Read Only Memory (Erasable Programmable Read Only Memory; hereinafter referred to as: EPROM) or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device , or any suitable combination of the above. As used herein, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a data signal carrying computer readable program code in baseband or as part of a carrier wave. Such propagated data signals may take many forms, including - but not limited to - electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction 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, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for performing the operations of the present application may be written in one or more programming languages or combinations thereof, including object-oriented programming languages—such as Java, Smalltalk, C++, and conventional Procedural Programming Language - such as "C" or a similar programming language. 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 cases involving a remote computer, the remote computer may be connected to the user computer through any kind of network, including a Local Area Network (hereinafter referred to as LAN) or a Wide Area Network (hereinafter referred to as WAN), or may be connected to to an external computer (eg via an Internet connection using an Internet Service Provider).

In the description of this specification, descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific examples", or "some examples" mean that specific features described in connection with the embodiment or example , structure, material or characteristic is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the described specific features, structures, materials or characteristics may be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples and features of different embodiments or examples described in this specification without conflicting with each other.

In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present application, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

Any process or method descriptions in flowcharts or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing custom logical functions or steps of a process , and the scope of preferred embodiments of the present application includes additional implementations in which functions may be performed out of the order shown or discussed, including in substantially simultaneous fashion or in reverse order depending on the functions involved, which shall It should be understood by those skilled in the art to which the embodiments of the present application belong.

Depending on the context, the word "if" as used herein may be interpreted as "at" or "when" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrases "if determined" or "if detected (the stated condition or event)" could be interpreted as "when determined" or "in response to the determination" or "when detected (the stated condition or event) )" or "in response to detection of (a stated condition or event)".

It should be noted that the terminals involved in the embodiments of the present application may include, but are not limited to, personal computers (PersonalComputer; hereinafter referred to as: PC), personal digital assistants (PersonalDigital Assistant; hereinafter referred to as: PDA), wireless handheld devices, tablet computers ( Tablet Computer), mobile phone, MP3 player, MP4 player, etc.

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

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware, or in the form of hardware plus software functional units.

The above-mentioned integrated units implemented in the form of software functional units may be stored in a computer-readable storage medium. The above-mentioned software functional units are stored in a storage medium, and include several instructions to enable a computer device (which may be a personal computer, server, or network device, etc.) or a processor (Processor) to execute the methods described in various embodiments of the present application. partial steps. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as ROM), random access memory (Random Access Memory; hereinafter referred to as RAM), magnetic disk or optical disc, etc. A medium on which program code can be stored.

The above is only a preferred embodiment of the application, and is not intended to limit the application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the application should be included in the application. within the scope of protection.

Claims (10)

1. a face picture collection method, is characterized in that, described method comprises: Collect multiple face sample pictures of multiple users through multiple image acquisition devices with different acquisition parameters, and obtain a face sample picture set corresponding to each image acquisition device; Extract the face sample pictures from the face sample picture sets corresponding to each image acquisition device according to the preset ratio, and form a face picture collection based on the extracted face sample pictures; Comparing the obtained picture to be recognized with the face sample picture in the collection of face pictures to obtain a face sample picture matching the picture to be recognized. 2. method according to claim 1, is characterized in that, before described by a plurality of image acquisition devices with different acquisition parameters respectively collecting a plurality of face sample pictures of a plurality of users, also comprising: Configure the acquisition parameters for each image acquisition device. 3. method according to claim 1, is characterized in that, described by a plurality of image acquisition devices with different acquisition parameters respectively collects a plurality of face sample pictures of a plurality of users comprising: Each image acquisition device captures the faces of multiple users multiple times to obtain a face sample picture set corresponding to each image acquisition device. 4. method according to claim 1, is characterized in that, described by a plurality of image acquisition devices with different acquisition parameters collects respectively a plurality of face sample pictures of a plurality of users, and obtains each image acquisition device corresponding The face sample image collection also includes: Detecting each face sample picture, and comparing the quality of each face sample picture with a preset picture quality, and, When the quality of the human face sample picture is not lower than the preset picture quality, output the human face sample picture to a corresponding human face sample picture set. 5. A kind of face picture collection device, is characterized in that, described device comprises: The collection module is used to collect a plurality of face sample pictures of multiple users through a plurality of image collection devices with different collection parameters, and obtain a set of face sample pictures corresponding to each image collection device; The extraction module is used to extract face sample pictures from the set of face sample pictures corresponding to each image acquisition device collected by the acquisition module according to a preset ratio, and form a face picture based on the extracted face sample pictures Collection; A comparison module, configured to compare the obtained picture to be identified with the face sample picture in the collection of human face pictures extracted by the extraction module, to obtain a face sample picture that matches the picture to be identified . 6. The device according to claim 5, characterized in that, the device further comprises: The configuration module is used to configure the acquisition parameters of each image acquisition device. 7. The device of claim 5, wherein: The acquisition module is specifically used to capture multiple times of faces of multiple users through each image acquisition device, so as to obtain a face sample picture set corresponding to each image acquisition device. 8. The device according to claim 5, characterized in that, the device further comprises: A detection module, configured to detect each face sample picture collected by the acquisition module, and compare the quality of each face sample picture with a preset picture quality, and, When the quality of the human face sample picture is not lower than the preset picture quality, output the human face sample picture to a corresponding human face sample picture set. 9. A computer device, characterized by comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, when the processor executes the computer program, the The method described in any one of 1 to 4 is required. 10. A non-transitory computer-readable storage medium on which a computer program is stored, wherein the computer program implements the method according to any one of claims 1-4 when executed by a processor.