CN111860069A

CN111860069A - Image processing method and system

Info

Publication number: CN111860069A
Application number: CN201910361587.1A
Authority: CN
Inventors: 周仁义; 寇浩锋; 胡跃祥; 汪翔; 周强
Original assignee: Baidu com Times Technology Beijing Co Ltd; Baidu USA LLC
Current assignee: Baidu com Times Technology Beijing Co Ltd; Baidu USA LLC
Priority date: 2019-04-30
Filing date: 2019-04-30
Publication date: 2020-10-30

Abstract

The embodiment of the application discloses an image processing method and system. One embodiment of the method comprises: the processor is used for determining a target face area image from the face area image set and outputting the target face area image to the at least one identification AI chip; the at least one AI chip is used for carrying out face recognition on the target face area image to obtain a recognition result. The method and the device for identifying the target face area image adopt the processor to determine the target face area image, so that the face area image received by the AI chip can be directly identified. The system of the embodiment can adopt each recognition AI chip with stronger processing capacity to be dedicated to face recognition, and does not need other steps such as face detection and the like, so as to improve the efficiency of face recognition.

Description

Image processing method and system

Technical Field

The embodiment of the application relates to the technical field of computers, in particular to the technical field of internet, and particularly relates to an image processing method and system.

Background

With the development of Artificial Intelligence (AI) technology, Artificial Intelligence has been more and more widely used. Machines that implement artificial intelligence can think like humans, making reactions similar or even identical to those of humans. Artificial intelligence has many applications, such as image processing, natural language processing, and so forth.

In the related art, tasks such as generating response sentences or processing images occupy more computing resources, so the task completion efficiency is often low.

Disclosure of Invention

The embodiment of the application provides an image processing method and system.

In a first aspect, an embodiment of the present application provides an image processing system, including a processor and at least one recognition AI chip; the processor is used for determining a target face area image from the face area image set and outputting the target face area image to at least one identification AI chip; and the AI chip is used for carrying out face recognition on the target face area image to obtain a recognition result.

In some embodiments, the system further comprises an AI detection chip, the face region image set being a face region image queue; the detection AI chip is used for acquiring an image to be detected, determining a face area containing a face from the image to be detected in response to the detection that the image to be detected contains the face, and outputting the image to be detected and the face area to the processor; and the processor is also used for generating a face area image in the image to be detected based on the face area and adding the generated face area image into the face area image queue.

In some embodiments, the system is for a terminal device.

In some embodiments, different recognition AI chips perform different face recognition tasks.

In some embodiments, the face recognition task for face recognition includes at least one of: determining face attribute information corresponding to the target face region image, determining face features of the target face region image, searching similar images of the target face region image in a base by using the face features, and determining whether a face contained in the face region image is a living body.

In some embodiments, each of the at least one recognition AI chip performs face recognition in parallel.

In some embodiments, the processor is further configured to output the target face area image for each of the at least one recognition AI chip.

In some embodiments, the processor is further configured to select at least one target recognition AI chip from the at least one recognition AI chip based on the scheduling information, and output the target face area image to the at least one target recognition AI chip.

In some embodiments, the connection between the detection AI chip and the processor, and between the processor and the at least one identification AI chip is via USB.

In some embodiments, the processor is further configured to receive an identification result of the at least one identification AI chip.

In some embodiments, the system further comprises a camera; and the detection AI chip is further used for receiving the image to be detected shot by the camera.

In a second aspect, an embodiment of the present application provides an image processing method for a processor of an electronic device, where the electronic device includes the processor and at least one recognition AI chip; the method comprises the following steps: and determining a target face area image from the face area image set, and outputting the target face area image to at least one identification AI chip.

In some embodiments, the electronic device further includes an AI detection chip, the face region image set is a face region image queue, and the method further includes: receiving an image to be detected output by an AI chip and a face area containing a face in the image to be detected; and generating a face region image in the image to be detected based on the face region, and adding the generated face region image into a face region image queue.

In some embodiments, the electronic device is a terminal device.

In some embodiments, outputting the target face region image to at least one recognition AI chip comprises: and outputting the target face area image for each recognition AI chip in the at least one recognition AI chip.

In some embodiments, outputting the target face region image to at least one recognition AI chip comprises: and based on the scheduling information, selecting at least one target recognition AI chip from the at least one recognition AI chip, and outputting the target face area image to the at least one target recognition AI chip.

In some embodiments, the method further comprises: and receiving the recognition result of the face recognition of at least one recognition AI chip.

In a third aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a storage device for storing one or more programs which, when executed by one or more processors, cause the one or more processors to implement a method as in any embodiment of the image processing method.

In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements a method as in any one of the embodiments of the image processing method.

In a fifth aspect, an embodiment of the present application provides an electronic device, including: an interface; a memory having one or more programs stored thereon; and one or more processors, operatively connected to the interface and the memory, for: and determining a target face area image from the face area image set, and outputting the target face area image to at least one identification AI chip.

In a sixth aspect, embodiments of the present application provide a computer-readable storage medium having a computer program stored thereon, where the computer program, when executed by one or more processors, causes the one or more processors to: and determining a target face area image from the face area image set, and outputting the target face area image to at least one identification AI chip.

According to the image processing scheme provided by the embodiment of the application, firstly, a processor is used for determining a target face area image from a face area image set and outputting the target face area image to at least one identification AI chip; and the AI chip is used for carrying out face recognition on the target face area image to obtain a recognition result. The method and the device for identifying the target face area image adopt the processor to determine the target face area image, so that the preprocessing is carried out before face identification by the processor, and the face area image received by the AI chip can be directly subjected to face identification. In general, the face recognition needs to occupy a lot of computing resources, and the system of this embodiment may adopt each AI chip with a strong processing capability to concentrate on the face recognition, and does not need to perform other steps such as face detection, so as to improve the efficiency of the offline face recognition. And the face recognition efficiency can be further improved by adopting at least one AI chip for face recognition.

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 schematic block diagram of one embodiment of an image processing system according to the present application;

FIG. 3 is a schematic block diagram of one embodiment of an image processing system according to the present application;

FIG. 4 is a flow diagram of one embodiment of an image processing method according to the present application;

FIG. 5 is a schematic block diagram of a computer system suitable for use in implementing an electronic device 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 image processing method or image processing system of the present application may be applied.

As shown in fig. 1, system architecture 100 may include terminal device 101, network 102, and server 103. Network 102 is the medium used to provide communication links between terminal devices 101 and server 103. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

A user may use terminal device 101 to interact with server 103 over network 102 to receive or send messages and the like. Various communication client applications, such as an image processing application, a video application, a live application, an instant messaging tool, a mailbox client, social platform software, and the like, may be installed on the terminal device 101.

Here, the terminal apparatus 101 may be hardware or software. When the terminal device 101 is hardware, it may be various electronic devices with a display screen, including but not limited to a smart phone, a tablet computer, an e-book reader, a laptop portable computer, a desktop computer, and the like. The terminal device may include a processor, and an identification AI chip for face recognition. When the terminal apparatus 101 is software, it can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., multiple pieces of software or software modules to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.

The server 103 may be a server that provides various services, such as a background server that provides support for the terminal device 101. The background server may analyze and perform other processing on the received data such as the image, and feed back a processing result (e.g., a result of face recognition) to the terminal device.

It should be noted that the image processing method provided in the embodiment of the present application may be executed by the server 103 or the terminal device 101, and accordingly, the image processing apparatus may be disposed in the server 103 or the terminal device 101.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow 200 of one embodiment of an image processing system according to the present application is shown. The image processing system includes a processor 210 and at least one recognition AI chip 220.

The processor 210 is configured to determine a target face region image from the face region image set, and output the target face region image to the at least one AI recognition chip 220;

in this embodiment, a face region image set may exist in the processor 210 of an electronic device (for example, the terminal device shown in fig. 1), and the processor 210 may determine at least one face region image from the face region image set as a target face region image. Thereafter, the processor 210 may output the target face region image to at least one recognition AI chip 220.

The face region image is an image containing a face region, which contains no or little content other than the face region. The face region may be presented in a rectangular or other shape, containing various portions of the face. Furthermore, some parts of the face may not be included in the face regions, such as the ears and chin. The face area images in the face area image set may be pre-stored in the processor 210, or may be sent to the processor 210 by other devices of the electronic device or other electronic devices.

The processor 210 may be various processors, such as an ARM processor (Advanced RISC Machines), an Intel processor (Intel processor), or an MIPS processor (microprocessor with out Interlocked pipelined microprocessors), among others. The AI recognition chip is an artificial intelligence chip for face recognition.

In practice, one or more face region images may be included in the set of face region images. The execution subject can determine the target face area image in the set in various ways. For example, the execution subject may use the image that is added to the set first as the target face area image. In addition, the execution subject may further use the face region image with the preset label as the target face region image.

The at least one AI recognition chip 220 is configured to perform face recognition on the target face region image to obtain a recognition result.

In this embodiment, the at least one AI recognition chip 220 may perform face recognition on the target face region image to obtain a recognition result. Specifically, the processor 210 and the identification AI chip in the electronic device may be connected in various ways, such as USB connection.

In some optional implementations of the embodiment, the processor 210 may be further configured to receive an identification result of at least one identification AI chip 220.

In these alternative implementations, the processor 210 may receive the recognition results of the AI chips to aggregate the recognition results of different recognition processes, so as to utilize the recognition results subsequently.

In some optional implementations of this embodiment, different recognition AI chips perform different face recognition tasks.

In these alternative implementations, different recognition AI chips may contain different execution instructions and/or models to implement different face recognition tasks. The realization modes adopt different AI chips to execute different face recognition tasks, can avoid repeated execution of the tasks and enable the images to be processed more variously.

In some optional application scenarios of these implementations, the face recognition task corresponding to the face recognition includes at least one of the following:

determining face attribute information corresponding to the target face region image, determining face features of the target face region image, searching similar images of the target face region image in a base by using the face features, and determining whether a face contained in the face region image is a living body.

In these alternative application scenarios, the face attribute information refers to the attributes of the person, such as gender, age, etc., that can be determined by the face. Specifically, when the AI chip performs the base library recognition, the facial features of the target face region image may be determined, and the facial features may be compared with the facial features of the face region images in the base library to find similar facial features in the base library. Then, the face region image corresponding to the similar facial feature in the base library may be used as a similar image. The similar facial features may be the facial features with the highest similarity to the facial features of the target face region image in the base library, or the facial features with the similarity to the facial features of the target face region image exceeding a preset similarity threshold in the base library. In practice, the recognition AI chip may determine whether a face contained in the face area image is a living body by using living body detection, that is, whether a photographic subject of the photographic device at the time of photographing the face area image is a real person.

The application scenes can utilize different AI chips to execute different face recognition tasks so as to perform various and quick recognition on the face region images.

In some optional application scenarios of these implementations, the respective recognition AI chips of the at least one recognition AI chip 220 perform face recognition in parallel.

In these optional application scenarios, the at least one recognition AI chip 220 may perform the face recognition process in parallel, so as to speed up the processing of the face region image by the at least one recognition AI chip 220.

In some optional application scenarios of these implementations, the processor 210 may be further configured to output the target face area image for each of the at least one recognition AI chip 220.

In these optional application scenarios, the processor 210 may output the target face region image to each recognition AI chip in the electronic device. Different recognition AI chips perform different face recognition tasks, and thus target face region images can be output to the respective recognition AI chips. In this way, the target face region image may be subjected to different face recognition analyses, and the execution subject may obtain a very comprehensive analysis result regarding the target face region image.

In some optional application scenarios of these implementations, the processor 210 is further configured to select at least one target recognition AI chip from the at least one recognition AI chip 220 based on the scheduling information, and output the target face area image to the at least one target recognition AI chip.

In these optional application scenarios, the processor 210 may select at least one identified AI chip from the at least one identified AI chip 220 as a target identified AI chip based on the scheduling information. And, the execution subject may output the target face region image to the target recognition AI chip. Specifically, the scheduling information is used to indicate to which recognition AI chip the target face area image is output. The scheduling information may be pre-stored in the processor 210, or may be transmitted by other devices of the electronic device or other electronic devices.

In practice, the scheduling information may indicate the identified AI chip to be selected in a variety of ways. For example, the scheduling information may directly indicate the recognition AI chip qualified to acquire the image of the target face region. For example, the AI identification chips include three AI identification chips, i.e., No. 1, No. 2, and No. 3, and the scheduling information may indicate that No. 2 and No. 3 are qualified for obtaining the face area image, so the processor 210 selects No. 2 and No. 3 as the target AI identification chip. Alternatively, the scheduling information may also indicate different timings for acquiring the face region image by the AI chip. For example, the AI identification chips are three, No. 1, No. 2, and No. 3, and the scheduling information may instruct the processor 210 to output the odd-numbered target face area images to No. 1 and No. 2, and the even-numbered target face area images to No. 3.

In some optional implementations of this embodiment, the system is for a terminal device.

In these alternative implementations, the image processing system described above may be applied to a terminal device. Therefore, the off-line face recognition of the terminal equipment can be better realized, and the real-time face recognition efficiency of the terminal equipment is improved.

In the method provided by the above embodiment of the present application, the processor 210 is used to determine the target face region image, so that the processor 210 performs preprocessing before face recognition, and the AI chip receives the face region image and can directly perform face recognition. In general, the face recognition needs to occupy a lot of computing resources, and the system of this embodiment may adopt each AI chip with a strong processing capability to concentrate on the face recognition, and does not need to perform other steps such as face detection, so as to improve the efficiency of the offline face recognition. And the face recognition efficiency can be further improved by adopting at least one recognition AI chip 220 for face recognition.

With further reference to fig. 3, yet another embodiment 300 of an image processing system is shown. The image processing system 300 further includes an AI detection chip, and the face region image sets are face region image queues.

The AI detection chip 310 is configured to obtain an image to be detected, determine a face region including a face from the image to be detected in response to detecting that the image to be detected includes the face, and output the image to be detected and the face region to the processor.

In this embodiment, the AI detection chip 310 may obtain an image to be detected, and in response to detecting that the image to be detected includes a face, may determine a face region including the face from the image to be detected. The detection AI chip 310 may then output the image to be detected and the determined face region to the processor 320. Specifically, the face detection may detect a position of a face, so that the AI detection chip 310 may determine whether the image to be detected includes the face through the face detection, and determine a face region when the image includes the face. The face region may be represented by a coordinate point and/or a length of a side of the face region. For example, the face region may be a rectangle, and the face region may be expressed as a vertex coordinate of the face region and a width length and a height length of the face region in the image to be detected. The detection AI chip 310 herein refers to an artificial intelligence chip for image detection in an electronic device.

In some optional implementations of this embodiment, the system further includes a camera 340; the detection AI chip 310 is further configured to receive an image to be detected, which is captured by the camera 340.

In these alternative implementations, the detection AI chip 310 may obtain an image to be detected by receiving the image to be detected captured by the camera 340. Specifically, the detection AI chip 310 may receive the image to be detected through a variety of interfaces, such as an MIPI (Mobile Industry Processor Interface).

The processor 320 is further configured to generate a face region image in the image to be detected based on the face region, and add the generated face region image to the face region image queue.

In this embodiment, the processor 320 may further receive the image to be detected output by the detection AI chip 310 and the determined face region. And the execution main body can generate a face region image in the image to be detected based on the face region, and add the generated face region image into the face region image queue. Specifically, the processor 320 may perform matting on an image to be detected, so as to determine a local image where a face region is located from the image to be detected, that is, determine a face region image.

The detection AI chip 310 in this embodiment may enable the processor 320 to perform processes such as determining a face region image, so that the detection AI chip 310 is dedicated to face detection, thereby improving the efficiency of face detection.

In practice, the processor 320 determines and outputs the target face region image from the face region image queue by using a first-in first-out principle. That is, the processor 210 may output the face region images that are first entered into the queue in the face region image queue as the target face region images.

In some optional implementations of the present embodiment, the detection AI chip 310 and the processor 320, and the processor 320 and the at least one identification AI chip 330 are connected through USB.

In these alternative implementations, the connections between the processor 320 and the detection AI chip and between the processor 320 and the identification AI chip can be made through USB, which facilitates fast transmission of information. The USB interface does not need to be powered off when being connected and disconnected with other devices, and is more convenient to be communicated with other devices.

With further reference to fig. 4, the present application also provides an image processing method for a processor of an electronic device, the electronic device comprising a processor and at least one recognition AI chip; the method comprises the following steps: step 401, determining a target face region image from the face region image set, and outputting the target face region image to at least one AI identification chip.

In some embodiments, the electronic device further includes an AI detection chip, and the face region image set is a face region image queue, and the method further includes: receiving an image to be detected output by an AI chip and a face area containing a face in the image to be detected; and generating a face region image in the image to be detected based on the face region, and adding the generated face region image into a face region image queue.

In some embodiments, the electronic device is a terminal device.

In some embodiments, the method further comprises: step 402, receiving an identification result of at least one AI chip for face identification.

In the method provided by the embodiment of the application, the target face area image is determined by the processor, and the face area image received by the AI chip can be directly identified. In general, the face recognition needs to occupy a lot of computing resources, and the system of this embodiment may adopt each AI chip with a strong processing capability to concentrate on the face recognition, and does not need to perform other steps such as face detection, so as to improve the efficiency of the face recognition. And the face recognition efficiency can be further improved by adopting at least one AI chip for face recognition.

Further, as an implementation of any of the methods described above, the present application provides an embodiment of an image processing apparatus for a processor of an electronic device, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 4, and the apparatus is particularly applicable to various electronic devices.

The above-mentioned electronic device includes a processor and at least one recognition AI chip, and the image processing apparatus for a processor of this embodiment includes: a determination unit. The determining unit is configured to determine a target face area image from the face area image set, and output the target face area image to the at least one AI chip.

In some embodiments, the determining unit of the image processing apparatus for the processor may determine at least one face region image from the set of face region images as the target face region image. Then, the processor may output the target face region image to at least one recognition AI chip.

In some optional implementation manners of this embodiment, the electronic device further includes an AI detection chip, the face region image set is a face region image queue, and the apparatus further includes: the receiving unit is configured to receive an image to be detected output by the AI chip and a face area containing a face in the image to be detected; and the generating unit is configured to generate a face area image in the image to be detected based on the face area and add the generated face area image into the face area image queue.

In some optional implementation manners of this embodiment, the electronic device is a terminal device.

In some optional implementations of this embodiment, the determining unit is further configured to: and outputting the target face area image for each recognition AI chip in the at least one recognition AI chip.

In some optional implementations of this embodiment, the determining unit is further configured to: and based on the scheduling information, selecting at least one target recognition AI chip from the at least one recognition AI chip, and outputting the target face area image to the at least one target recognition AI chip.

In some optional implementations of this embodiment, the detection AI chip and the processor, and the processor and the at least one identification AI chip are connected by USB.

In some optional implementations of this embodiment, the apparatus further includes: and a result receiving unit configured to receive a recognition result of the face recognition by the at least one recognition AI chip.

Further, as an implementation of any of the above methods, the present application provides an electronic device, comprising: an interface; a memory having one or more programs stored thereon; and one or more processors, operatively connected to the interface and the memory, for: and determining a target face area image from the face area image set, and outputting the target face area image to at least one identification AI chip.

Further, as an implementation of any of the above methods, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by one or more processors, causes the one or more processors to: and determining a target face area image from the face area image set, and outputting the target face area image to at least one identification AI chip.

As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.

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 in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of embodiments of the present disclosure. It should be noted that the computer readable medium of the embodiments of the present disclosure may 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 embodiments of the disclosure, 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 embodiments of the present disclosure, however, a computer readable signal medium may comprise 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: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

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 which 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 a determination unit. The names of these units do not in some cases constitute a limitation on the unit itself, and for example, the determination unit may also be described as a "unit that determines a target face region image from a set of face region images and outputs the target face region image to at least one recognition AI chip".

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: and determining a target face area image from the face area image set, and outputting the target face area image to at least one identification AI chip.

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

1. An image processing system includes a processor and at least one recognition AI chip;

The processor is used for determining a target face area image from the face area image set and outputting the target face area image to the at least one identification AI chip;

and the at least one identification AI chip is used for carrying out face identification on the target face area image to obtain an identification result.

2. The system of claim 1, wherein the system further comprises a detection AI chip, the set of face region images being a face region image queue;

the detection AI chip is used for acquiring an image to be detected, determining a face area containing a face from the image to be detected in response to the detection that the image to be detected contains the face, and outputting the image to be detected and the face area to the processor;

the processor is further configured to generate a face region image in the image to be detected based on the face region, and add the generated face region image to the face region image queue.

3. The system of claim 1, wherein the system is for a terminal device.

4. The system of claim 1, wherein different recognition AI chips perform different face recognition tasks.

5. The system of claim 4, wherein the face recognition task for face recognition comprises at least one of:

6. The system of claim 4, wherein each of the at least one recognition AI chip performs face recognition in parallel.

7. The system of claim 4, wherein,

the processor is further configured to output the target face area image for each of the at least one recognition AI chip.

8. The system of claim 4, wherein,

the processor is further configured to select at least one target recognition AI chip from the at least one recognition AI chip based on the scheduling information, and output the target face area image to the at least one target recognition AI chip.

9. The system of claim 2, wherein the detection AI chip and the processor, and the processor and the at least one identification AI chip are connected via USB.

10. The system of claim 1, wherein,

the processor is further configured to receive an identification result of the at least one identification AI chip.

11. The system of claim 2, wherein the system further comprises a camera;

the AI detection chip is further used for receiving the image to be detected shot by the camera.

12. An image processing method for a processor of an electronic device, the electronic device comprising a processor and at least one recognition AI chip; the method comprises the following steps:

and determining a target face area image from the face area image set, and outputting the target face area image to the at least one identification AI chip.

13. The method of claim 12, wherein the electronic device further comprises a detect AI chip, the set of face region images is a queue of face region images, the method further comprising:

receiving an image to be detected output by the AI chip, and a face area containing a face in the image to be detected;

and generating a face region image in the image to be detected based on the face region, and adding the generated face region image into the face region image queue.

14. The method according to claim 12, wherein the outputting the target face region image to the at least one recognition AI chip comprises:

and outputting the target face area image for each recognition AI chip in the at least one recognition AI chip.

15. The method according to claim 12, wherein the outputting the target face region image to the at least one recognition AI chip comprises:

and selecting at least one target recognition AI chip from the at least one recognition AI chip based on the scheduling information, and outputting the target face area image to the at least one target recognition AI chip.

16. The method of claim 12, wherein the method further comprises:

and receiving the recognition result of the at least one AI chip for face recognition.

17. An electronic device, comprising:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement a method as claimed in any one of claims 12-16.

18. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 12-16.