CN108875452A - Face identification method, device, system and computer-readable medium - Google Patents

Face identification method, device, system and computer-readable medium Download PDF

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Publication number
CN108875452A
CN108875452A CN201710329940.9A CN201710329940A CN108875452A CN 108875452 A CN108875452 A CN 108875452A CN 201710329940 A CN201710329940 A CN 201710329940A CN 108875452 A CN108875452 A CN 108875452A
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face
facial image
identification
image
acquired
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孙磊
杜宇飞
王靖易
郑韬
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Beijing Megvii Technology Co Ltd
Beijing Maigewei Technology Co Ltd
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Beijing Megvii Technology Co Ltd
Beijing Maigewei Technology Co Ltd
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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
    • G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • 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
    • G06V40/165—Detection; Localisation; Normalisation using facial parts and geometric relationships
    • 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
    • G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • 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
    • G06V40/40—Spoof detection, e.g. liveness detection
    • G06V40/45—Detection of the body part being alive

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  • Oral & Maxillofacial Surgery (AREA)
  • Health & Medical Sciences (AREA)
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  • Bioinformatics & Computational Biology (AREA)
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Abstract

The present invention provides a kind of face identification method, device, system and computer-readable medium, the face identification method includes：Image queue is formed for same object to be identified acquisition facial image；Determine whether face images are qualified facial image in the queue；Top-quality facial image is selected in qualified facial image；And recognition of face is carried out to the top-quality facial image.Face identification method, device, system and computer-readable medium according to an embodiment of the present invention are in recognition of face without all handling acquired each frame facial image, but only top-quality facial image in qualified facial image is handled, it not only can guarantee the accuracy of face recognition result, calculation amount is also greatly reduced simultaneously, computing resource has been saved, recognition efficiency is improved.

Description

Face identification method, device, system and computer-readable medium
Technical field
The present invention relates to technical field of face recognition, relate more specifically to a kind of face identification method, device, system and meter Calculation machine readable medium.
Background technique
Application based on recognition of face is increasingly appearing in people's lives.The basic procedure of recognition of face is exactly The acquisition for carrying out facial image first, is then based on recognizer and carries out similarity calculation in face database, to obtain The result of one identification.
In existing face identification method, image collecting device usually all sends its collected each frame image to Identification module, correspondingly, identification module for image acquisition device to each frame image will handle, though in this way Crucial picture frame will not be so missed, but increases calculation amount, wastes computing resource.
Summary of the invention
Propose the present invention to solve the above-mentioned problems.According to an aspect of the present invention, a kind of recognition of face side is provided Method, the face identification method include：Image queue is formed for same object to be identified acquisition facial image；Determine the team Whether face images are qualified facial image in column；Top-quality face figure is selected in qualified facial image Picture；And recognition of face is carried out to the top-quality facial image.
In one embodiment of the invention, whether face images are qualified face in the determination queue The step of image includes：For the acquired facial image of each frame in the queue, determine that at least one the following is No satisfaction identification requires：The 3 d pose of face in the acquired facial image；The acquired facial image Fog-level；The occlusion state of face in the acquired facial image；The brightness of the acquired facial image.
In one embodiment of the invention, whether full to the 3 d pose, the fog-level, the occlusion state The determination that foot identification requires is carried out based on depth convolutional network.
In one embodiment of the invention, determine the face in the acquired facial image 3 d pose whether Meet identification to require to include：Determine the angle per the one-dimensional positive face of deviation of the face in three dimensions；And it is if described It is not more than predetermined threshold per the one-dimensional angle for deviateing positive face, it is determined that the 3 d pose of the face meets identification and requires, conversely, Identification is then unsatisfactory for require.
In one embodiment of the invention, determine whether the fog-level of the acquired facial image meets identification It requires to include：Motion blur and Gaussian Blur based on the acquired facial image determine the acquired facial image Fog-level；And if the fog-level of the acquired facial image is not more than predetermined threshold, it is determined that the people The fog-level of face image meets identification and requires, and requires conversely, being then unsatisfactory for identification.
In one embodiment of the invention, determine the face in the acquired facial image occlusion state whether Meet identification to require to include：Determine whether the key position of the face is blocked；And if the face key position It is not blocked, it is determined that the occlusion state of the face in the facial image meets identification and requires, and wants conversely, being then unsatisfactory for identification It asks.
In one embodiment of the invention, whether what identification required is met to the brightness of the acquired facial image Determination is carried out based on grey level histogram.
In one embodiment of the invention, the face identification method further includes：It lives to the object to be identified Physical examination is surveyed.
In one embodiment of the invention, the In vivo detection is to be based on executing instruction movement to the object to be identified Facial image carry out, including judge whether instruction movement qualified and divides using preparatory trained skin elasticity Class device judge the object to be identified execute instruction movement before and after skin area image whether be in skin of living body at least One of.
In one embodiment of the invention, the In vivo detection is based on structured light patterns in the object to be identified What the sub-surface scattering degree in face carried out.
In one embodiment of the invention, the face identification method further includes：Before acquiring facial image or It is whether suitable that current illumination condition is detected during acquiring facial image, if illumination condition is improper, starts benefit Electro-optical device carries out light filling.
In one embodiment of the invention, the face identification method further includes：Before acquiring facial image first really Determine whether object to be identified enters shooting area.
In one embodiment of the invention, the face identification method further includes：Scanning people is shown to object to be identified The animation of face is acquiring and is identifying facial image with prompt.
In one embodiment of the invention, the face identification method further includes：When failing to acquire in the given time When to qualified facial image or to facial image recognition failures, prompt the object to be identified adjustment posture to resurvey people Face image.
In one embodiment of the invention, described to select top-quality facial image packet in qualified facial image It includes：Quality score is carried out to the facial image of the qualification, selects the highest facial image of score as top-quality face Image；Wherein, carrying out quality score to the facial image of the qualification includes：The face figure is determined based on depth convolutional network The fog-level of picture, the mass fraction are defined as 1 and subtract fog-level.
According to a further aspect of the invention, a kind of face identification device is provided, the face identification device includes：Image is adopted Collect module, for forming image queue for same object to be identified acquisition facial image, determines all faces in the queue Whether image is qualified facial image, and selects top-quality facial image to send identification in qualified facial image Module；And the identification module, for carrying out recognition of face to the top-quality facial image.
In one embodiment of the invention, described image acquisition module is further used for：For every in the queue The acquired facial image of one frame, determines whether at least one the following meets identification and require：The acquired face figure The 3 d pose of face as in；The fog-level of the acquired facial image；In the acquired facial image The occlusion state of face；The brightness of the acquired facial image.
In one embodiment of the invention, described image acquisition module is to the 3 d pose, the fog-level, institute Stating occlusion state whether to meet the determination that identification requires is carried out based on depth convolutional network.
In one embodiment of the invention, described image acquisition module determines the people in the acquired facial image Whether the 3 d pose of face, which meets identification, requires to include：Determine the angle per the one-dimensional positive face of deviation of the face in three dimensions Degree；And if described be not more than predetermined threshold per the one-dimensional angle for deviateing positive face, it is determined that the 3 d pose of the face is full Foot identification requires, and requires conversely, being then unsatisfactory for identification.
In one embodiment of the invention, described image acquisition module determines the fuzzy of the acquired facial image Whether degree, which meets identification, requires to include：Described in motion blur and Gaussian Blur based on the acquired facial image determine The fog-level of acquired facial image；And if the fog-level of the acquired facial image is not more than predetermined threshold Value, it is determined that the fog-level of the facial image meets identification and requires, and requires conversely, being then unsatisfactory for identification.
In one embodiment of the invention, described image acquisition module determines the people in the acquired facial image Whether the occlusion state of face, which meets identification, requires to include：Determine whether the key position of the face is blocked；And if institute The key position for stating face is not blocked, it is determined that the occlusion state of the face in the facial image meets identification and requires, instead It, then be unsatisfactory for identification and require.
In one embodiment of the invention, described image acquisition module is to the brightness of the acquired facial image The no determination for meeting identification requirement is carried out based on grey level histogram.
In one embodiment of the invention, the face identification device further includes：In vivo detection module, for described Object to be identified carries out In vivo detection.
In one embodiment of the invention, the In vivo detection module is based on executing instruction the object to be identified dynamic The facial image of work carries out In vivo detection, and the In vivo detection includes judging whether the instruction movement is qualified and uses pre- First trained skin elasticity classifier judges that skin area image of the object to be identified before and after executing instruction movement is No is at least one of skin of living body.
In one embodiment of the invention, the In vivo detection module is based on structured light patterns in the object to be identified Face in sub-surface scattering degree carry out In vivo detection.
In one embodiment of the invention, the face identification device further includes：Illumination detection module, for described Whether current illumination condition is detected before image capture module acquisition facial image or during acquiring facial image Properly, if illumination condition is improper, start light compensating apparatus and carry out light filling.
In one embodiment of the invention, the face identification device further includes：Apart from detection module, for described First determine whether object to be identified enters shooting area before image capture module acquisition facial image.
In one embodiment of the invention, the face identification device further includes：Display module is used for to be identified right As the animation of display scanning face, facial image is being acquired and identified with prompt.
In one embodiment of the invention, the display module is also used to：When failing to collect conjunction in the given time The facial image of lattice or when to facial image recognition failures, prompts the object to be identified adjustment posture to resurvey face figure Picture.
In one embodiment of the invention, described image acquisition module is further used for：To the face figure of the qualification As carrying out quality score, select the highest facial image of score as top-quality facial image；Wherein, to the qualification Facial image carries out quality score：The fog-level of the facial image, the quality are determined based on depth convolutional network Score definition subtracts fog-level for 1.
According to a further aspect of the present invention, a kind of face identification system is provided, the face identification system includes that image passes Sensor, storage device and processor, described image sensor are stored on the storage device for acquiring facial image by institute The computer program of processor operation is stated, the computer program executes described in any of the above-described when being run by the processor Face identification method.
Another aspect according to the present invention provides a kind of computer-readable medium, stores on the computer-readable medium There is computer program, the computer program executes face identification method described in any of the above embodiments at runtime.
Face identification method, device, system and computer-readable medium according to an embodiment of the present invention are in recognition of face Without all handling acquired each frame facial image, but only to top-quality face in qualified facial image Image is handled, and not only can guarantee the accuracy of face recognition result, while also greatly reducing calculation amount, is saved Computing resource, improves recognition efficiency.
Detailed description of the invention
The embodiment of the present invention is described in more detail in conjunction with the accompanying drawings, the above and other purposes of the present invention, Feature and advantage will be apparent.Attached drawing is used to provide to further understand the embodiment of the present invention, and constitutes explanation A part of book, is used to explain the present invention together with the embodiment of the present invention, is not construed as limiting the invention.In the accompanying drawings, Identical reference label typically represents same parts or step.
Fig. 1 shows for realizing face identification method according to an embodiment of the present invention, device, system and computer-readable Jie The schematic block diagram of the exemplary electronic device of matter；
Fig. 2 shows the schematic flow charts of face identification method according to an embodiment of the present invention；
Fig. 3 shows determine facial image whether show by He Ge method in face identification method according to an embodiment of the present invention Meaning property flow chart；
Fig. 4 shows the schematic flow chart of face identification method according to another embodiment of the present invention；
Fig. 5 shows the schematic block diagram of face identification device according to an embodiment of the present invention；And
Fig. 6 shows the schematic block diagram of face identification system according to an embodiment of the present invention.
Specific embodiment
In order to enable the object, technical solutions and advantages of the present invention become apparent, root is described in detail below with reference to accompanying drawings According to example embodiments of the present invention.Obviously, described embodiment is only a part of the embodiments of the present invention, rather than this hair Bright whole embodiments, it should be appreciated that the present invention is not limited by example embodiment described herein.Based on described in the present invention The embodiment of the present invention, those skilled in the art's obtained all other embodiment in the case where not making the creative labor It should all fall under the scope of the present invention.
Firstly, describing the face identification method for realizing the embodiment of the present invention, device, system and calculating referring to Fig.1 The exemplary electronic device 100 of machine readable medium.
As shown in Figure 1, electronic equipment 100 include one or more processors 102, it is one or more storage device 104, defeated Enter device 106, output device 108 and imaging sensor 110, these components pass through bus system 112 and/or other forms The interconnection of bindiny mechanism's (not shown).It should be noted that the component and structure of electronic equipment 100 shown in FIG. 1 are only exemplary, and Unrestricted, as needed, the electronic equipment also can have other assemblies and structure.
The processor 102 can be central processing unit (CPU) or have data-handling capacity and/or instruction execution The processing unit of the other forms of ability, and the other components that can control in the electronic equipment 100 are desired to execute Function.
The storage device 104 may include one or more computer program products, and the computer program product can To include various forms of computer readable storage mediums, such as volatile memory and/or nonvolatile memory.It is described easy The property lost memory for example may include random access memory (RAM) and/or cache memory (cache) etc..It is described non- Volatile memory for example may include read-only memory (ROM), hard disk, flash memory etc..In the computer readable storage medium On can store one or more computer program instructions, processor 102 can run described program instruction, to realize hereafter institute The client functionality (realized by processor) in the embodiment of the present invention stated and/or other desired functions.In the meter Can also store various application programs and various data in calculation machine readable storage medium storing program for executing, for example, the application program use and/or The various data etc. generated.
The input unit 106 can be the device that user is used to input instruction, and may include keyboard, mouse, wheat One or more of gram wind and touch screen etc..
The output device 108 can export various information (such as image or sound) to external (such as user), and It may include one or more of display, loudspeaker etc..
Described image sensor 110 can be shot facial image (such as photo, video etc.), and by captured image It is stored in the storage device 104 for the use of other components.Image collecting device 110 can be camera.It should be appreciated that Image collecting device 110 is only example, and electronic equipment 100 can not include image collecting device 110.In this case, may be used To utilize other image acquisition device facial images, and the facial image of acquisition is sent to electronic equipment 100.
Illustratively, for realizing face identification method according to an embodiment of the present invention, device, system and computer-readable The exemplary electronic device of medium may be implemented as smart phone, tablet computer etc..
In the following, face identification method 200 according to an embodiment of the present invention will be described with reference to Fig. 2.
In step S210, image queue is formed for same object to be identified acquisition facial image.
In step S220, determine whether face images are qualified facial image in the queue.
In one embodiment, figure can be formed for same object to be identified acquisition facial image by image collecting device Judge whether the face images in image queue are qualified facial image as queue, and by image collecting device.Generally Ground, image collecting device may be for an object acquisition multiple images or video flowing to be identified, in the embodiment of the present invention In, image collecting device, which may not need, to be completely transferred to identification module for its acquired image and handles, and can only by Qualified facial image is transmitted to identification module and is handled, and can not only reduce the data volume of transmission in this way, moreover it is possible to reduce and know The data volume of other places reason further, since transmission is qualified facial image, therefore can also guarantee the accurate of identifying processing Property.In other embodiments, determine whether acquired facial image is that qualified this work of facial image can not also be by Image collecting device is completed, and is completed by other modules or device.
In one embodiment, qualified facial image can be understood as the facial image for meeting recognition of face requirement.Such as The acquired facial image of fruit meets recognition of face requirement, it is determined that acquired facial image is qualified facial image；Instead It, if acquired facial image is unsatisfactory for recognition of face requirement, it is determined that acquired facial image is underproof people Face image.According to the different application scene of recognition of face, recognition of face requires to be slightly different.Generally, recognition of face is wanted Asking may include the basic demand for enabling to face normally to be identified.
Illustratively, determine that the step of whether acquired facial image is qualified facial image can include determining that down Whether column items, which meet identification, requires：The 3 d pose of face in acquired facial image；Acquired facial image Fog-level (i.e. fuzziness)；The occlusion state of face in acquired facial image；The brightness of acquired facial image. In one embodiment, these are all satisfied identification and require, and just determine that acquired facial image is qualified facial image.? In other embodiments, at least partly satisfaction in these, which identifies, to be required, then acquired facial image can be determined for qualification Facial image.It describes to determine whether facial image closes in face identification method according to an embodiment of the present invention below with reference to Fig. 3 The schematic flow chart of the method 300 of lattice.
As shown in figure 3, determining whether the brightness of acquired facial image meets identification and require in step S310.If The brightness of acquired facial image meets identification and requires, then continues to step S320；, whereas if acquired face The brightness of image is unsatisfactory for identification and requires, then skips to step S360.
In one embodiment, it can be based on to whether the brightness of acquired facial image meets the determination that identification requires Grey level histogram carries out.It in one example, can be to the face in facial image in face entirety, eye part, right eye portion Divide and mouth respectively extracts grey level histogram feature, obtains four histograms, calculate aforementioned four histogram and its 30% and 70% The brightness of quantile differs greatly if there is two or more numerical value and normal illumination face corresponding data, is then judged as The brightness of facial image is unsatisfactory for identification and requires, and is otherwise judged as that satisfaction identification requires.It in other examples, can also be by appointing What his suitable mode requires to determine whether the brightness of acquired facial image meets identification.
In step S320, determine whether the fog-level of acquired facial image meets identification and require.If acquired Facial image fog-level meet identification require, then continue to step S330；, whereas if acquired face figure The fog-level of picture is unsatisfactory for identification and requires, then skips to step S360.
It in one embodiment, can be with to whether the fog-level of acquired facial image meets determination that identification requires It is carried out based on depth convolutional network.In one example, determine whether the fog-level of acquired facial image meets identification It is required that may include：Motion blur and Gaussian Blur based on acquired facial image determine the mould of acquired facial image Paste degree；If the fog-level of acquired facial image is not more than predetermined threshold, it is determined that the fog-level of facial image Meet identification to require, be required conversely, being then unsatisfactory for identification.It can be implemented based on the good depth convolutional network model of off-line training The process.Wherein, the setting of the predetermined threshold can be based on specific application scenarios.It in other examples, can also be by appointing What his suitable mode requires to determine whether the fog-level of acquired facial image meets identification.
In step S330, determine whether the occlusion state of the face in acquired facial image meets identification and require.Such as The occlusion state of face in the acquired facial image of fruit meets identification and requires, then continues to step S340；Conversely, such as The occlusion state of face in the acquired facial image of fruit is unsatisfactory for identification and requires, then skips to step S360.
In one embodiment, whether what identification required is met to the occlusion state of the face in acquired facial image Determination can be carried out based on depth convolutional network.In one example, blocking for the face in acquired facial image is determined Whether state meets identification requirement：Determine whether the key position of face is blocked；If the key position of face It is not blocked, it is determined that the occlusion state of the face in facial image meets identification and requires, and requires conversely, being then unsatisfactory for identification. Wherein, the key position of face may include at least one of organs such as eyes, mouth.For example, in one example, it can be right The eyes and mouth of face carry out shadowing.Using the good depth convolutional network model of off-line training, according to the face figure of input Whether picture, output three key positions of left-eye/right-eye/mouth are blocked.If any one position is blocked, facial image Identification is unsatisfactory for require.In other examples, acquired face figure can also be determined by any other suitable mode Whether the occlusion state of the face as in, which meets identification, requires.
In step S340, determine whether the 3 d pose of the face in acquired facial image meets identification and require.Such as The 3 d pose of face in the acquired facial image of fruit meets identification and requires, then continues to step S350；Conversely, such as The 3 d pose of face in the acquired facial image of fruit is unsatisfactory for identification and requires, then skips to step S360.
In one embodiment, whether what identification required is met to the 3 d pose of the face in acquired facial image Determination can be carried out based on depth convolutional network.In one example, the three-dimensional of the face in acquired facial image is determined Whether posture meets identification requirement：Determine the angle per the one-dimensional positive face of deviation of face in three dimensions；If It is not more than predetermined threshold per the one-dimensional angle for deviateing positive face, it is determined that the 3 d pose of face meets identification and requires, conversely, then not Meet identification to require.It can implement the process based on the good depth convolutional network model of off-line training.If side face to the left and right Angle be greater than or equal to predetermined threshold (such as 30 degree), or bow and face upward brilliance degree and be greater than or equal to predetermined threshold (such as 30 Degree), it is determined that the 3 d pose of the face in acquired facial image is unsatisfactory for identification and requires.In other examples, may be used Determine whether the 3 d pose of the face in acquired facial image meets identification in a manner of suitable by any other It is required that.
In step S350, determine that acquired facial image is qualified facial image.
In step S360, determine that acquired facial image is underproof facial image.
It describes above exemplarily and determines whether facial image closes in face identification method according to an embodiment of the present invention The schematic flow of the method for lattice.Although being walked it is worth noting that, being described as in the process includes step S310 to S360 The sequence of rapid S310 to S340 is merely exemplary and not restrictive, and step S310 to step S340 can be suitable in no particular order Sequence.In actual application, different priority or weight can also be arranged to the judgement of step S310 to step S340, It in addition it is also possible to need not include the whole of these steps, or also may include that other additional steps are further to carry out Judgement, to meet the needs of practical application, the invention is not limited in this regard.
The step of continuing to describe face identification method 200 according to an embodiment of the present invention referring back to Fig. 2 below.
In step S230, top-quality facial image is selected in qualified facial image.
In one embodiment, when for an object to be identified there are when the facial image of multiframe qualification, can be therefrom Top-quality facial image is selected to carry out recognition of face for sending identification module to.It in one example, can be to this A little facial images carry out quality score, and (such as mass fraction can subtract the value that fuzziness obtains for 1, and wherein fuzziness is 0 to 1 Between numerical value), it is highest for being handled in subsequent identification step then therefrom to select score, after being further reduced The calculation amount of continuous processing.Wherein, fuzziness can be used housebroken neural net regression and obtain, for example, one image of input To neural network, the fuzzy score of this image is exported as fuzziness.Further, if 3 d pose, coverage extent, light If line judgement all qualifications, mass fraction is 1-fuzziness, and otherwise mass fraction is 0-fuzziness, if obtaining one negative point, It is just directly filtered, quality score is carried out to all images in image queue in this way and sorts below, then select Top-quality facial image.
In other examples, it can also be selected from qualified facial image using other suitable modes top-quality Facial image.
In one embodiment, step S230 can be implemented by image collecting device, can also be by other modules or dress It sets to implement.
In step S240, recognition of face is carried out to the top-quality facial image.
In an embodiment of the present invention, recognition of face described in step S240 can be the method for known recognition of face. It will be appreciated, however, that the present invention is not limited by the method for recognition of face, the method for either existing recognition of face or future The method of the recognition of face of exploitation can be applied in face identification method 200 according to an embodiment of the present invention, and also answer Including within the scope of the present invention.
Based on above description, face identification method according to an embodiment of the present invention is in recognition of face without to acquired Each frame facial image all handled, but only to top-quality facial image in qualified facial image at Reason, not only can guarantee the accuracy of face recognition result, while also greatly reducing calculation amount, save calculating money Source improves recognition efficiency.
Illustratively, face identification method according to an embodiment of the present invention can be in setting with memory and processor It is realized in standby, device or system.
In addition, face identification method according to an embodiment of the present invention can also be deployed in server end (or cloud).Substitution Ground, face identification method according to an embodiment of the present invention can also be deployed in server end (or cloud) and personal terminal with being distributed Place.
In a further embodiment, face identification method according to an embodiment of the present invention can also include：To be identified Object carries out In vivo detection.Wherein the step of In vivo detection can the recognition of face the step of before implement, can also be in face Implement after the step of identification.Preferably, the recognition of face the step of before implement In vivo detection, to improve the property of recognition of face Energy and efficiency.
In a further embodiment, face identification method according to an embodiment of the present invention can also include：In acquisition people It is whether suitable that current illumination condition is detected before face image or during acquiring facial image, if illumination condition is not Properly, then start light compensating apparatus and carry out light filling.
In a further embodiment, face identification method according to an embodiment of the present invention can also include：In acquisition people First determine whether object to be identified enters shooting area before face image.
In a further embodiment, face identification method according to an embodiment of the present invention can also include：To be identified The animation of object display scanning face, is acquiring and is identifying facial image with prompt.
In a further embodiment, face identification method according to an embodiment of the present invention can also include：When predetermined Fail to collect qualified facial image in time or when to facial image recognition failures, prompts the object to be identified adjustment appearance State is to resurvey facial image.
These further embodiments can be individually or real together with above-mentioned face identification method 200 in combination with each other It applies, it is real according to the present invention to be advanced optimized in terms of improving recognition of face performance, saving computing resource, raising The face identification method for applying example is described in detail it below with reference to Fig. 4.
Fig. 4 shows the schematic flow chart of face identification method 400 according to another embodiment of the present invention.As shown in figure 4, Face identification method 400 may include steps of：
In step S410, determine whether object to be identified enters shooting area.If it is determined that object to be identified enters shooting Region then advances to step S420, conversely, then continuing implementation steps S410 itself.Step S410 can be used for determine to Identification object restarts the image collecting device for acquiring facial image when entering shooting area, can save power consumption in this way. In one embodiment, which can be realized by the way of infrared induction.
In step S420, whether suitable current illumination condition is detected.If current illumination condition is suitable, advance to Step S440, conversely, then advancing to step S430.Wherein, suitable illumination condition can be understood as such illumination condition, The brightness of facial image collected meets identification and requires under the illumination condition.Whether detect illumination condition properly can be based on pre- Threshold value is determined to judge.Step S420 can provide preferable basic condition for acquisition, the recognition of face of subsequent facial image, So that subsequent processing is highly efficient.In one embodiment, this can be implemented using sensor or other suitable modes Step.
In step S430, current illumination condition is adjusted, suitable degree is adjusted to, then proceeds by Step S440.For example, can for example start lighting device if detecting that current illumination is darker in step S420 and be mended Light.If detecting that current illumination is excessively bright, can take appropriate measures implementation adjustment.
In step S440, the facial image of object to be identified is acquired, and In vivo detection is carried out to object to be identified.If really Determining object to be identified is living body, then step S450 is continued to, conversely, then skipping to step S490.Object to be identified is carried out In vivo detection can effectively guard against the attack of the various ways such as photo, video, 3D faceform or mask.Specifically, right The In vivo detection that object to be identified carries out can be carried out using following manner.
In one embodiment, photo or video can be only shot, in addition vacation face in cloud judges, for some pairs of safety It is required that weak scene.Illustratively, the photo or video taken for object to be identified can be uploaded to cloud service Device detects the face in photo or video by cloud server and judges when detecting face the authenticity of face. For example, may include having trained true face classifier and false face classifier in cloud server.It is described in detail below by example logical It crosses shooting photo or video carries out the embodiment of In vivo detection, in order to understand.
In one example, it can indicate that object to be identified reads aloud passage, by acquiring facial image, judge its lip Dynamic whether move with the lip of corresponding text matches, if matching, In vivo detection success.
In one example, can indicate object to be identified make required movement (required movement be, for example, finger pressing It gulps down gas in two cheek skins or mouth to heave two cheeks).In an exemplary example, when object to be identified has done one Or when multiple instructions movement, acquire its facial image, judge whether its actions taken qualified, if so, In vivo detection success, Conversely, In vivo detection fails.In another exemplary example, when object to be identified has done one or more instruction movements When, the skin area image before capturing object to be identified movement in image respectively and after movement, and by skin area image It is transferred to skin elasticity classifier, which is the disaggregated model succeeded in school in advance.For example, if it is work Body skin, then model output is 1, and otherwise output is 0.In this embodiment it is possible to based on referring to object to be identified in execution Show that the comparison of the skin area image of movement front and back carries out In vivo detection.
Illustratively, the study of skin elasticity classifier can carry out offline.A kind of possible embodiment is to search in advance Collection living body true man do the before and after frames image of compulsory exercise, while collecting using photo, video playback, scraps of paper mask and 3D model Etc. the attack image for doing compulsory exercise.The former as positive sample, the latter as negative sample, then use deep learning, support to The statistical learning methods such as amount machine train skin elasticity classifier.
It illustratively, can be based on Face datection and face key point location algorithm come real to the capture of skin area image It is existing, such as a large amount of facial images are collected in advance, the canthus of face is manually marked out in every image, the corners of the mouth, the wing of nose, cheekbone is most High point, a series of key points such as outer profile point use machine learning algorithm (such as deep learning, or returning based on local feature Reduction method) and using the aforementioned image marked as input training Face datection, face key point location model.It will be collected After the facial image of movement front and back inputs trained Face datection, face key point location model, will output face location and Human face region is cut into a series of triangular plate members according to key point position coordinates, will be located at chin, cheekbone by key point position coordinates The triangular plate member image block in the regions such as bone, two cheeks is as face skin area.
In another embodiment, living body acquisition device, such as binocular camera can be done using special hardware, for one The higher scene of a little safety requirements.In this embodiment it is possible to the judgement based on the sub-surface scattering degree to face to be identified Carry out In vivo detection.Due to the sub-surface scattering degree of 3D mask etc. and true man's face it is different (when sub-surface scatters stronger, image Gradient is smaller, so that diffusion is smaller), for example, the sub-surface scattering degree of the mask of the materials such as general paper or plastics is remote It is weaker than face, and the sub-surface of the mask of the materials such as general silica gel scatters degree much stronger than face, therefore by diffusion Judgement can effectively defend mask attacker.It therefore, in embodiments of the present invention, can be by binocular camera and structure light knot It closes, has the 3D face of structured light patterns by binocular camera acquisition, then according to structured light patterns in 3D face sub-surface Scattering degree carries out living body judgement.
The In vivo detection that may include in face identification method according to an embodiment of the present invention is described above exemplarily Specific example.The embodiment of face identification method 400 according to an embodiment of the present invention is continued to describe now referring back to Fig. 4.
In step S450, determine whether acquired facial image is qualified facial image, if it is, continuing on To step S460, if it is not, then skipping to step S490.Wherein, the recognition of face that step S450 can be described with aforementioned combination Fig. 2 The step S220 of method 200 is similar, for sake of simplicity, details are not described herein again.
In step S460, recognition of face is carried out to qualified facial image.In one embodiment, before this step, The step S230 that can also implement the face identification method 200 of aforementioned combination Fig. 2 description, i.e., select from qualified facial image Top-quality facial image is can be further reduced calculation amount in this way, improve for carrying out recognition of face in step S460 Recognition accuracy.
In step S470, it is determined whether there are matched face recognition results.If it is, continuing to step S480, whereas if matched face recognition result is not present, i.e. recognition of face fails, then skips to step S490.
In step S480, face recognition result is exported.
In step S490, prompt object to be identified adjustment posture to resurvey facial image.If acquired face Image is unqualified or face recognition result fails, it may be possible to, can be not since the posture of object to be identified needs to adjust Qualified facial image can be collected or when to facial image recognition failures, prompt object to be identified adjustment posture to resurvey Facial image.
In addition, completing acquisition satisfaction identification requirement from starting to collect after object to be identified comes into coverage Facial image process, and be transmitted to identification module and identified, probably need regular hour (such as 1-2 seconds), can be with It shows the process of human face scanning in the form of animation on display terminal within this time, is prompting object to be identified system Acquisition and identification facial image, if failing to collect the image or knowledge for meeting that identification requires after the predetermined time (such as 2 seconds) Do not fail, then object to be identified adjustment posture is prompted to resurvey facial image.
The schematic flow of face identification method according to another embodiment of the present invention is described above exemplarily.It is based on Above description, face identification method according to another embodiment of the present invention can not only save computing resource, moreover it is possible to improve people Face recognition performance simultaneously improves user experience.
The face identification device of another aspect of the present invention offer is described below with reference to Fig. 5.Fig. 5 shows real according to the present invention Apply the schematic block diagram of the face identification device 500 of example.
As shown in figure 5, face identification device 500 according to an embodiment of the present invention includes image capture module 510 and identification Module 520.The modules can execute each step/function of the face identification method above in conjunction with Fig. 2 description respectively. Only the major function of each module of face identification device 500 is described below, and omits the details having been described above Content.
Image capture module 510 is used to form image queue for same object to be identified acquisition facial image, determines institute It states whether face images in queue are qualified facial image, and selects top-quality people in qualified facial image Face image sends identification module 520 to.Identification module 520 is used to carry out recognition of face to the top-quality facial image. Image capture module 510 and identification module 520 can the operation storage dresses of processor 102 in electronic equipment as shown in Figure 1 The program instruction that stores in 104 is set to realize.
In one embodiment, image capture module 510 may be implemented in image collecting device.Based on this, image is adopted Acquisition means, which may not need, to be completely transferred to identification module 520 for its acquired image and handles, and can only will be qualified Top-quality facial image is transmitted to identification module 520 and is handled in facial image, can not only reduce transmission in this way Data volume, moreover it is possible to the data volume of identifying processing is reduced, further, since that transmission is top-quality people in qualified facial image Face image, therefore can also guarantee the accuracy of identifying processing.
In one embodiment, qualified facial image can be understood as the facial image for meeting recognition of face requirement.Such as The acquired facial image of fruit meets recognition of face requirement, it is determined that acquired facial image is qualified facial image；Instead It, if acquired facial image is unsatisfactory for recognition of face requirement, it is determined that acquired facial image is underproof people Face image.According to the different application scene of recognition of face, recognition of face requires to be slightly different.Generally, recognition of face is wanted Asking may include the basic demand for enabling to face normally to be identified.
Illustratively, image capture module 510 determine acquired facial image whether be qualified facial image step Suddenly it can include determining that whether the following meets identification and require：The 3 d pose of face in acquired facial image；Through The fog-level of the facial image of acquisition；The occlusion state of face in acquired facial image；Acquired facial image Brightness.In one embodiment, these are all satisfied identification and require, and just determine that acquired facial image is qualified face Image.In other embodiments, at least partly satisfaction in these, which identifies, requires, then can determine acquired facial image For qualified facial image.It can understand in conjunction with Fig. 3 and determine facial image in face identification method according to an embodiment of the present invention Whether He Ge method, for sake of simplicity, details are not described herein again.
In one embodiment, face identification device 500 can also include In vivo detection module (not shown in FIG. 5), In vivo detection module is used to carry out In vivo detection to object to be identified.In one example, In vivo detection module can based on pair The facial image that the object to be identified executes instruction movement carries out In vivo detection, and the In vivo detection includes judging the instruction Movement it is whether qualified and using preparatory trained skin elasticity classifier judge the object to be identified execute instruction it is dynamic Whether the skin area image for making front and back is at least one of skin of living body.In another example, In vivo detection module can To carry out In vivo detection based on sub-surface scattering degree of the structured light patterns in the face of the object to be identified.It can combine Description understands the work of the In vivo detection module in face identification device according to an embodiment of the present invention as described in step S440 in Fig. 4 Body detection process, for sake of simplicity, details are not described herein again.In vivo detection module may be implemented in image collecting device, can also be with Realize other than image collecting device other modules or device in.
In one embodiment, face identification device 500 can also include illumination detection module (not shown in FIG. 5), Illumination detection module is used to examine before image capture module 510 acquires facial image or during acquiring facial image It whether suitable surveys current illumination condition, if illumination condition is improper, starts light compensating apparatus and carry out light filling.It can be in conjunction with figure Description understands the behaviour of the illumination detection module in face identification device according to an embodiment of the present invention as described in step S420 in 4 Make, for sake of simplicity, details are not described herein again.Illumination detection module may be implemented in image collecting device, also may be implemented scheming As other than acquisition device other modules or device in.
In one embodiment, face identification device 500 can also include apart from detection module (not shown in FIG. 5), It is used to before image capture module 510 acquires facial image first determine whether object to be identified enters shooting apart from detection module Region.Can in conjunction in Fig. 4 as described in step S410 in description understanding face identification device according to an embodiment of the present invention away from Operation from detection module, for sake of simplicity, details are not described herein again.It may be implemented in image collecting device apart from detection module, Also may be implemented other than image collecting device other modules or device in.
In one embodiment, face identification device 500 can also include display module (not shown in FIG. 5), display Module is used to show the animation of scanning face to object to be identified, is acquiring and identifying facial image with prompt.In a reality It applies in example, when failing to collect qualified facial image in the given time or to facial image recognition failures, the display Module is also used to prompt the object to be identified adjustment posture to resurvey facial image.It can combine in Fig. 4 about step The description of S490 understands the operation of the display module in face identification device according to an embodiment of the present invention, for sake of simplicity, herein It repeats no more.Display module may be implemented on the display terminal of recognition of face front end or realize in other suitable modules or dress In setting.
The structure composition of face identification device according to an embodiment of the present invention is described above exemplarily, for sake of simplicity, Only the major function of each module is described, and omits the detail content having been noted above previously in conjunction with Fig. 2 to Fig. 4.
Based on above description, face identification device according to an embodiment of the present invention is in recognition of face without to acquired Each frame facial image all handled, but only to top-quality facial image in qualified facial image at Reason, not only can guarantee the accuracy of face recognition result, while also greatly reducing calculation amount, save calculating money Source improves recognition efficiency.In addition, face identification device according to an embodiment of the present invention can also improve recognition of face performance, and Improve user experience.
Fig. 6 shows the schematic block diagram of face identification system 600 according to an embodiment of the present invention.Face identification system 600 include imaging sensor 610, storage device 620 and processor 630.
Wherein, imaging sensor 610 is used to acquire the facial image of object to be identified.The storage of storage device 620 is for real The program code of corresponding steps in existing face identification method according to an embodiment of the present invention.Processor 630 is for running storage The program code stored in device 620 to execute the corresponding steps of face identification method according to an embodiment of the present invention, and is used Corresponding module in realization face identification device according to an embodiment of the present invention.
In one embodiment, when said program code is run by processor 630 face identification system 600 is executed Following steps：Determining the face images in the image queue for the facial image formation of same object to be identified acquisition is The no facial image for qualification；Top-quality facial image is selected in qualified facial image；And most to the quality Good facial image carries out recognition of face.
In one embodiment, when said program code is run by processor 630 face identification system 600 is executed The determination queue in face images the step of whether being qualified facial image include：For in the queue The acquired facial image of each frame, determine whether at least one the following meets identification and require：The acquired people The 3 d pose of face in face image；The fog-level of the acquired facial image；The acquired facial image In face occlusion state；The brightness of the acquired facial image.
In one embodiment, when said program code is run by processor 630 face identification system 600 is executed To the 3 d pose, the fog-level, the occlusion state whether meet identification require determination be based on depth roll up Product network carries out.
In one embodiment, when said program code is run by processor 630 face identification system 600 is executed The determination acquired facial image in the 3 d pose of face whether meet identification and require to include：Determine the face The angle per the one-dimensional positive face of deviation in three dimensions；And make a reservation for if the angle per the one-dimensional positive face of deviation is not more than Threshold value, it is determined that the 3 d pose of the face meets identification and requires, and requires conversely, being then unsatisfactory for identification.
In one embodiment, when said program code is run by processor 630 face identification system 600 is executed The fog-level of the determination acquired facial image whether meet identification and require to include：Based on the acquired face The motion blur and Gaussian Blur of image determine the fog-level of the acquired facial image；And it is if described acquired The fog-level of facial image be not more than predetermined threshold, it is determined that the fog-level of the facial image meets identification and requires, It is required conversely, being then unsatisfactory for identification.
In one embodiment, when said program code is run by processor 630 face identification system 600 is executed The determination acquired facial image in the occlusion state of face whether meet identification and require to include：Determine the face Key position whether be blocked；And if the key position of the face is not blocked, it is determined that in the facial image Face occlusion state meet identification require, conversely, be then unsatisfactory for identification require.
In one embodiment, when said program code is run by processor 630 face identification system 600 is executed To the brightness of the acquired facial image whether meet identification require determination be to be carried out based on grey level histogram.
In one embodiment, hold face identification system 600 when said program code is run by processor 630 Row following steps：In vivo detection is carried out to object to be identified.
In one embodiment, when said program code is run by processor 630 face identification system 600 is executed The In vivo detection be carried out based on the facial image for executing instruction movement to the object to be identified, including judgement described in Whether instruction movement is qualified and judges that the object to be identified refers in execution using preparatory trained skin elasticity classifier Whether the skin area image for showing movement front and back is at least one of skin of living body.
In one embodiment, when said program code is run by processor 630 face identification system 600 is executed The In vivo detection be based on structured light patterns in the face of the object to be identified sub-surface scattering degree carry out.
In one embodiment, hold face identification system 600 when said program code is run by processor 630 Row following steps：Whether current illumination condition is detected before acquiring facial image or during acquiring facial image Properly, if illumination condition is improper, start light compensating apparatus and carry out light filling.
In one embodiment, hold face identification system 600 when said program code is run by processor 630 Row following steps：First determine whether object to be identified enters shooting area before acquiring facial image.
In one embodiment, hold face identification system 600 when said program code is run by processor 630 Row following steps：The animation of scanning face is shown to object to be identified, is acquiring and identifying facial image with prompt.
In one embodiment, hold face identification system 600 when said program code is run by processor 630 Row following steps：When failing to collect qualified facial image in the given time or to facial image recognition failures, prompt The object to be identified adjustment posture is to resurvey facial image.
In addition, according to embodiments of the present invention, additionally providing a kind of storage medium, storing program on said storage Instruction, when described program instruction is run by computer or processor for executing the face identification method of the embodiment of the present invention Corresponding steps, and for realizing the corresponding module in face identification device according to an embodiment of the present invention.The storage medium It such as may include the storage card of smart phone, the storage unit of tablet computer, the hard disk of personal computer, read-only memory (ROM), Erasable Programmable Read Only Memory EPROM (EPROM), portable compact disc read-only memory (CD-ROM), USB storage, Or any combination of above-mentioned storage medium.The computer readable storage medium can be one or more computer-readable deposit Any combination of storage media.
In one embodiment, the computer program instructions may be implemented real according to the present invention when being run by computer Each functional module of the face identification device of example is applied, and/or recognition of face according to an embodiment of the present invention can be executed Method.
In one embodiment, the computer program instructions make computer or place when being run by computer or processor It manages device and executes following steps：Determine the owner in the image queue for the facial image formation of same object to be identified acquisition Whether face image is qualified facial image；Top-quality facial image is selected in qualified facial image；And to institute It states top-quality facial image and carries out recognition of face.
In one embodiment, the computer program instructions make computer or place when being run by computer or processor Manage that the step of whether face images are qualified facial image in the determination queue that device executes includes：For institute The acquired facial image of each frame in queue is stated, determines whether at least one the following meets identification and require：The warp The 3 d pose of face in the facial image of acquisition；The fog-level of the acquired facial image；It is described acquired The occlusion state of face in facial image；The brightness of the acquired facial image.
In one embodiment, the computer program instructions make computer or place when being run by computer or processor What reason device executed is base to whether the 3 d pose, the fog-level, the occlusion state meet the determination that identification requires It is carried out in depth convolutional network.
In one embodiment, the computer program instructions make computer or place when being run by computer or processor Whether the 3 d pose for the face in the determination acquired facial image that reason device executes, which meets identification, requires to include：It determines The angle per the one-dimensional positive face of deviation of the face in three dimensions；And if it is described per the one-dimensional angle for deviateing positive face not Greater than predetermined threshold, it is determined that the 3 d pose of the face meets identification and requires, and requires conversely, being then unsatisfactory for identification.
In one embodiment, the computer program instructions make computer or place when being run by computer or processor Whether the fog-level for the determination acquired facial image that reason device executes, which meets identification, requires to include：Based on described through adopting The motion blur and Gaussian Blur of the facial image of collection determine the fog-level of the acquired facial image；And if institute The fog-level of acquired facial image is stated no more than predetermined threshold, it is determined that the fog-level of the facial image, which meets, to be known It does not require, is required conversely, being then unsatisfactory for identification.
In one embodiment, the computer program instructions make computer or place when being run by computer or processor Whether the occlusion state for the face in the determination acquired facial image that reason device executes, which meets identification, requires to include：It determines Whether the key position of the face is blocked；And if the key position of the face is not blocked, it is determined that the people The occlusion state of face in face image meets identification and requires, and requires conversely, being then unsatisfactory for identification.
In one embodiment, the computer program instructions make computer or place when being run by computer or processor What reason device executed is based on grey level histogram to whether the brightness of the acquired facial image meets the determination that identification requires It carries out.
In one embodiment, the computer program instructions also make when being run by computer or processor computer or Processor executes following steps：In vivo detection is carried out to object to be identified.
In one embodiment, the computer program instructions make computer or place when being run by computer or processor Reason device execute the In vivo detection be carried out based on the facial image for executing instruction movement to the object to be identified, including Judge whether the instruction movement is qualified and judges the object to be identified using preparatory trained skin elasticity classifier Whether the skin area image before and after executing instruction movement is at least one of skin of living body.
In one embodiment, the computer program instructions make computer or place when being run by computer or processor The In vivo detection that reason device executes is the sub-surface scattering journey based on structured light patterns in the face of the object to be identified What degree carried out.
In one embodiment, the computer program instructions also make when being run by computer or processor computer or Processor executes following steps：Current illumination is detected before acquiring facial image or during acquiring facial image Whether condition is suitable, if illumination condition is improper, starts light compensating apparatus and carries out light filling.
In one embodiment, the computer program instructions also make when being run by computer or processor computer or Processor executes following steps：First determine whether object to be identified enters shooting area before acquiring facial image.
In one embodiment, the computer program instructions also make when being run by computer or processor computer or Processor executes following steps：The animation of scanning face is shown to object to be identified, is acquiring and identifying face figure with prompt Picture.
In one embodiment, the computer program instructions also make when being run by computer or processor computer or Processor executes following steps：When failing to collect qualified facial image in the given time or to facial image recognition failures When, prompt the object to be identified adjustment posture to resurvey facial image.
Each module in face identification device according to an embodiment of the present invention can pass through people according to an embodiment of the present invention The processor computer program instructions that store in memory of operation of the electronic equipment of face identification realize, or can be in root The computer instruction stored in computer readable storage medium according to the computer program product of the embodiment of the present invention is by computer It is realized when operation.
Face identification method, device, system and computer-readable medium according to an embodiment of the present invention are in recognition of face Shi Wuxu handles acquired each frame facial image, but only to top-quality people in qualified facial image Face image is handled, and not only can guarantee the accuracy of face recognition result, while also greatly reducing calculation amount, section About computing resource, improves recognition efficiency.In addition, face identification method according to an embodiment of the present invention, device, system and Computer-readable medium can also improve recognition of face performance, and improve user experience.
Although describing example embodiment by reference to attached drawing here, it should be understood that above example embodiment are only exemplary , and be not intended to limit the scope of the invention to this.Those of ordinary skill in the art can carry out various changes wherein And modification, it is made without departing from the scope of the present invention and spiritual.All such changes and modifications are intended to be included in appended claims Within required the scope of the present invention.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure Member and algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually It is implemented in hardware or software, the specific application and design constraint depending on technical solution.Professional technician Each specific application can be used different methods to achieve the described function, but this realization is it is not considered that exceed The scope of the present invention.
In several embodiments provided herein, it should be understood that disclosed device and method can pass through it Its mode is realized.For example, apparatus embodiments described above are merely indicative, for example, the division of the unit, only Only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components can be tied Another equipment is closed or is desirably integrated into, or some features can be ignored or not executed.
In the instructions provided here, numerous specific details are set forth.It is to be appreciated, however, that implementation of the invention Example can be practiced without these specific details.In some instances, well known method, structure is not been shown in detail And technology, so as not to obscure the understanding of this specification.
Similarly, it should be understood that in order to simplify the present invention and help to understand one or more of the various inventive aspects, To in the description of exemplary embodiment of the present invention, each feature of the invention be grouped together into sometimes single embodiment, figure, Or in descriptions thereof.However, the method for the invention should not be construed to reflect following intention：It is i.e. claimed The present invention claims features more more than feature expressly recited in each claim.More precisely, such as corresponding power As sharp claim reflects, inventive point is that the spy of all features less than some disclosed single embodiment can be used Sign is to solve corresponding technical problem.Therefore, it then follows thus claims of specific embodiment are expressly incorporated in this specific Embodiment, wherein each, the claims themselves are regarded as separate embodiments of the invention.
It will be understood to those skilled in the art that any combination pair can be used other than mutually exclusive between feature All features disclosed in this specification (including adjoint claim, abstract and attached drawing) and so disclosed any method Or all process or units of equipment are combined.Unless expressly stated otherwise, this specification (is wanted including adjoint right Ask, make a summary and attached drawing) disclosed in each feature can be replaced with an alternative feature that provides the same, equivalent, or similar purpose.
In addition, it will be appreciated by those of skill in the art that although some embodiments described herein include other embodiments In included certain features rather than other feature, but the combination of the feature of different embodiments mean it is of the invention Within the scope of and form different embodiments.For example, in detail in the claims, embodiment claimed it is one of any Can in any combination mode come using.
Various component embodiments of the invention can be implemented in hardware, or to run on one or more processors Software module realize, or be implemented in a combination thereof.It will be understood by those of skill in the art that can be used in practice Microprocessor or digital signal processor (DSP) realize some moulds in article analytical equipment according to an embodiment of the present invention The some or all functions of block.The present invention is also implemented as a part or complete for executing method as described herein The program of device (for example, computer program and computer program product) in portion.It is such to realize that program of the invention can store On a computer-readable medium, it or may be in the form of one or more signals.Such signal can be from internet Downloading obtains on website, is perhaps provided on the carrier signal or is provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and ability Field technique personnel can be designed alternative embodiment without departing from the scope of the appended claims.In the claims, Any reference symbol between parentheses should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not Element or step listed in the claims.Word "a" or "an" located in front of the element does not exclude the presence of multiple such Element.The present invention can be by means of including the hardware of several different elements and being come by means of properly programmed computer real It is existing.In the unit claims listing several devices, several in these devices can be through the same hardware branch To embody.The use of word first, second, and third does not indicate any sequence.These words can be explained and be run after fame Claim.
The above description is merely a specific embodiment or to the explanation of specific embodiment, protection of the invention Range is not limited thereto, and anyone skilled in the art in the technical scope disclosed by the present invention, can be easily Expect change or replacement, should be covered by the protection scope of the present invention.Protection scope of the present invention should be with claim Subject to protection scope.

Claims (32)

1. a kind of face identification method, which is characterized in that the face identification method includes：
Image queue is formed for same object to be identified acquisition facial image；
Determine whether face images are qualified facial image in the queue；
Top-quality facial image is selected in qualified facial image；And
Recognition of face is carried out to the top-quality facial image.
2. face identification method according to claim 1, which is characterized in that all face figures in the determination queue Seem no includes for the step of qualified facial image：For the acquired facial image of each frame in the queue, determine Whether at least one the following, which meets identification, requires：
The 3 d pose of face in the acquired facial image；
The fog-level of the acquired facial image；
The occlusion state of face in the acquired facial image；
The brightness of the acquired facial image.
3. face identification method according to claim 2, which is characterized in that the 3 d pose, the fog-level, Whether the occlusion state, which meets the determination that identification requires, is carried out based on depth convolutional network.
4. face identification method according to claim 3, which is characterized in that determine in the acquired facial image Whether the 3 d pose of face, which meets identification, requires to include：
Determine the angle per the one-dimensional positive face of deviation of the face in three dimensions；And
If described be not more than predetermined threshold per the one-dimensional angle for deviateing positive face, it is determined that the 3 d pose of the face, which meets, to be known It does not require, is required conversely, being then unsatisfactory for identification.
5. face identification method according to claim 3, which is characterized in that determine the mould of the acquired facial image Whether paste degree, which meets identification, requires to include：
Motion blur and Gaussian Blur based on the acquired facial image determine the mould of the acquired facial image Paste degree；And
If the fog-level of the acquired facial image is not more than predetermined threshold, it is determined that the facial image obscures Degree meets identification and requires, and requires conversely, being then unsatisfactory for identification.
6. face identification method according to claim 3, which is characterized in that determine in the acquired facial image Whether the occlusion state of face, which meets identification, requires to include：
Determine whether the key position of the face is blocked；And
If the key position of the face is not blocked, it is determined that the occlusion state of the face in the facial image, which meets, to be known It does not require, is required conversely, being then unsatisfactory for identification.
7. face identification method according to claim 2, which is characterized in that the brightness to the acquired facial image Whether the determination that satisfaction identification requires is carried out based on grey level histogram.
8. face identification method described in any one of -7 according to claim 1, which is characterized in that the face identification method Further include：
In vivo detection is carried out to the object to be identified.
9. face identification method according to claim 8, which is characterized in that the In vivo detection is based on to described wait know What the facial image that other object executes instruction movement carried out, including judge whether the instruction movement is qualified and uses preparatory Whether trained skin elasticity classifier judges skin area image of the object to be identified before and after executing instruction movement For at least one of skin of living body.
10. face identification method according to claim 8, which is characterized in that the In vivo detection is based on structure light figure Sub-surface scattering degree of the case in the face of the object to be identified carries out.
11. face identification method described in any one of -7 according to claim 1, which is characterized in that the recognition of face side Method further includes：
It is whether suitable that current illumination condition is detected before acquiring facial image or during acquiring facial image, such as Fruit illumination condition is improper, then starts light compensating apparatus and carry out light filling.
12. face identification method described in any one of -7 according to claim 1, which is characterized in that the recognition of face side Method further includes：
First determine whether object to be identified enters shooting area before acquiring facial image.
13. face identification method described in any one of -7 according to claim 1, which is characterized in that the recognition of face side Method further includes：
The animation of scanning face is shown to object to be identified, is acquiring and identifying facial image with prompt.
14. face identification method according to claim 13, which is characterized in that the face identification method further includes：
When failing to collect qualified facial image in the given time or to facial image recognition failures, prompt described wait know Other object adjustment posture is to resurvey facial image.
15. face identification method according to claim 2, which is characterized in that described to be selected in qualified facial image Top-quality facial image includes：
Quality score is carried out to the facial image of the qualification, selects the highest facial image of score as top-quality face Image；
Wherein, carrying out quality score to the facial image of the qualification includes：
Determine that the fog-level of the facial image, the mass fraction are defined as 1 and subtract fuzzy journey based on depth convolutional network Degree.
16. a kind of face identification device, which is characterized in that the face identification device includes：
Image capture module determines the queue for forming image queue for same object to be identified acquisition facial image Whether middle face images are qualified facial image, and select top-quality facial image in qualified facial image Send identification module to；And
The identification module, for carrying out recognition of face to the top-quality facial image.
17. face identification device according to claim 16, which is characterized in that described image acquisition module is further used In：For the acquired facial image of each frame in the queue, determine whether at least one the following meets identification and want It asks：
The 3 d pose of face in the acquired facial image；
The fog-level of the acquired facial image；
The occlusion state of face in the acquired facial image；
The brightness of the acquired facial image.
18. face identification device according to claim 17, which is characterized in that described image acquisition module is to the three-dimensional Whether posture, the fog-level, the occlusion state, which meet the determination that identification requires, is carried out based on depth convolutional network.
19. face identification device according to claim 18, which is characterized in that described image acquisition module determines the warp Whether the 3 d pose of the face in the facial image of acquisition, which meets identification, requires to include：
Determine the angle per the one-dimensional positive face of deviation of the face in three dimensions；And
If described be not more than predetermined threshold per the one-dimensional angle for deviateing positive face, it is determined that the 3 d pose of the face, which meets, to be known It does not require, is required conversely, being then unsatisfactory for identification.
20. face identification device according to claim 18, which is characterized in that described image acquisition module determines the warp Whether the fog-level of the facial image of acquisition, which meets identification, requires to include：
Motion blur and Gaussian Blur based on the acquired facial image determine the mould of the acquired facial image Paste degree；And
If the fog-level of the acquired facial image is not more than predetermined threshold, it is determined that the facial image obscures Degree meets identification and requires, and requires conversely, being then unsatisfactory for identification.
21. face identification device according to claim 18, which is characterized in that described image acquisition module determines the warp Whether the occlusion state of the face in the facial image of acquisition, which meets identification, requires to include：
Determine whether the key position of the face is blocked；And
If the key position of the face is not blocked, it is determined that the occlusion state of the face in the facial image, which meets, to be known It does not require, is required conversely, being then unsatisfactory for identification.
22. face identification device according to claim 17, which is characterized in that described image acquisition module is to described through adopting Whether the brightness of the facial image of collection, which meets the determination that identification requires, is carried out based on grey level histogram.
23. face identification device described in any one of 6-22 according to claim 1, which is characterized in that the recognition of face Device further includes：
In vivo detection module, for carrying out In vivo detection to the object to be identified.
24. face identification device according to claim 23, which is characterized in that the In vivo detection module is based on to described The facial image that object to be identified executes instruction movement carries out In vivo detection, and the In vivo detection includes judging the instruction movement It is whether qualified and judge the object to be identified before executing instruction movement using preparatory trained skin elasticity classifier Whether skin area image afterwards is at least one of skin of living body.
25. face identification device according to claim 23, which is characterized in that the In vivo detection module is based on structure light Sub-surface scattering degree of the pattern in the face of the object to be identified carries out In vivo detection.
26. face identification device described in any one of 6-22 according to claim 1, which is characterized in that the recognition of face Device further includes：
Illumination detection module, for before described image acquisition module acquires facial image or in the mistake for acquiring facial image It is whether suitable that current illumination condition is detected in journey, if illumination condition is improper, is started light compensating apparatus and is carried out light filling.
27. face identification device described in any one of 6-22 according to claim 1, which is characterized in that the recognition of face Device further includes：
Apart from detection module, for first determined before described image acquisition module acquires facial image object to be identified whether into Enter shooting area.
28. face identification device described in any one of 6-22 according to claim 1, which is characterized in that the recognition of face Device further includes：
Display module is acquiring and is identifying facial image for showing the animation of scanning face to object to be identified with prompt.
29. face identification device according to claim 28, which is characterized in that the display module is also used to：
When failing to collect qualified facial image in the given time or to facial image recognition failures, prompt described wait know Other object adjustment posture is to resurvey facial image.
30. face identification device according to claim 17, which is characterized in that described image acquisition module is further used In：
Quality score is carried out to the facial image of the qualification, selects the highest facial image of score as top-quality face Image；
Wherein, carrying out quality score to the facial image of the qualification includes：
Determine that the fog-level of the facial image, the mass fraction are defined as 1 and subtract fuzzy journey based on depth convolutional network Degree.
31. a kind of face identification system, which is characterized in that the face identification system include imaging sensor, storage device and Processor, described image sensor are stored on the storage device and are run by the processor for acquiring facial image Computer program, the computer program are executed as described in any one of claim 1-15 when being run by the processor Face identification method.
32. a kind of computer-readable medium, which is characterized in that be stored with computer program, institute on the computer-readable medium It states computer program and executes face identification method as described in any one of claim 1-15 at runtime.
CN201710329940.9A 2017-05-11 2017-05-11 Face identification method, device, system and computer-readable medium Pending CN108875452A (en)

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Application publication date: 20181123