CN110211094A - Black eye intelligent determination method, device and computer readable storage medium - Google Patents
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Abstract
The present invention relates to a kind of artificial intelligence technologys, disclose a kind of black eye intelligent determination method, it include: reception face image set, the face image set is divided into positive sample collection and negative sample collection by tally set, after carrying out pretreatment operation to the positive sample collection and the negative sample collection, the tally set is input to black eye judgment models, the black eye judgment models are input to after the data of the positive sample collection and negative sample collection are carried out the operation of direction gradient histogram, the black eye judgment models are trained based on algorithm of support vector machine, training is exited when the loss function value in the algorithm of support vector machine is less than threshold value；Test set to the black eye judgment models for receiving user judge whether there is black eye, and export result.The present invention also proposes a kind of black eye intelligent judging device and a kind of computer readable storage medium.Accurately black eye intelligent judgment function may be implemented in the present invention.
Description
Technical field
The present invention relates to automated intelligents after field of artificial intelligence more particularly to a kind of input based on human face data
Judge black eye method, apparatus and computer readable storage medium.
Background technique
Black eye is due to staying up late, and mood swing is big, and asthenopia, aging lead to eye part skin vascular flow speed excessively
It is slowly formed viscous flow, tissue is for hypoxgia, and metabolic waste accumulation is excessive in blood vessel, causes eye pigmentation.Age is bigger
People, the subcutaneous fat around eyes becomes thinner, so black eye just becomes apparent from.It is black with the presence of many people in today's society
Eyelet is without knowing, it is therefore desirable to judge with the presence or absence of black eye, however, also depositing for black-eyed accurately identify
There are many problems, if application scenarios majority is more complicated, target shadow caused by the variation of the local dynamic station of background, uneven illumination
Etc. difficulty can be increased to identification, in addition, face is non-rigid targets, possess posture feature abundant, locating for same face
Different postures, often difference is very big in detection and identification.
Summary of the invention
The present invention provides a kind of black eye intelligent determination method, device and computer readable storage medium, main purpose
It is to provide a kind of scheme for realizing accurately black eye intelligent judgment function.
To achieve the above object, a kind of black eye intelligent determination method provided by the invention, comprising:
Data receiver layer receive face image set, the face image set by tally set be divided into positive sample collection with
Negative sample collection, the positive sample collection and the negative sample collection are carried out include gray processing, binaryzation and noise reduction pretreatment operation
Afterwards, the positive sample collection of the pretreatment completion and negative sample collection and the tally set are input to data analysis layer；
Data analysis layer receives the positive sample collection that pretreatment is completed and negative sample collection and the tally set, and will be described
Tally set is input to black eye judgment models, and the data of the positive sample collection and negative sample collection are carried out direction gradient histogram fortune
The black eye judgment models are input to after calculation, the black eye judgment models are based on algorithm of support vector machine to the negative sample
This collection and the positive sample collection are trained, and are moved back when the loss function value in the algorithm of support vector machine is less than threshold value
It trains out；
Data in the test set are mapped to higher-dimension sky based on nonlinear mapping method by the test set for receiving user
Between, the black eye judgment models judgement is input to after the test set that the mapping is completed is carried out the operation of direction gradient histogram
Whether there is black eye, and exports result.
Optionally, the data in the positive sample collection be include black-eyed face, the data in the negative sample collection are
It does not include black-eyed face.
Optionally, the noise reduction process uses self-adapting image denoising filter method:
G (x, y)=η (x, y)+f (x, y)
Wherein, (x, y) indicates that image slices vegetarian refreshments coordinate, f (x, y) are based on the self-adapting image denoising filter method pair
The positive sample collection and the negative sample collection carry out the output data after noise reduction process, and η (x, y) is noise, and g (x, y) is described
Positive sample collection and the negative sample collection,For the noise population variance of the positive sample collection and the negative sample collection,It is described
The pixel grey scale mean value of (x, y),For the pixel grey scale variance of (x, y), L indicates current pixel point coordinate.
Optionally, black to being input to after the data progress direction gradient histogram operation of the positive sample collection and negative sample collection
Eyelet judgment models, comprising:
Calculate the gradient magnitude and gradient direction value of each pixel (x, y) of data in the face image set, and by institute
Gradient magnitude is stated as the first component, the gradient direction value forms gradient matrix as second component；
Data in the gradient matrix are divided into multiple fritters, and are added the gradient magnitude and gradient direction of each fritter
Value obtains additive value, and the additive value connected to form gradient direction noxkata feature and be input to black eye judgment models.
Optionally, the algorithm of support vector machine includes Nonlinear Mapping and constraint solving；
The Nonlinear Mapping are as follows:
κ(xi, xj)=< θ (xi), θ (xj)>
Wherein, < θ (xi), θ (xj) > indicate the gradient direction noxkata feature (xi, xj) Nonlinear Mapping inner product meter
It calculates, κ (xi, xj) it is the gradient direction noxkata feature (xi, xj) nonlinear mapping function；
The constraint solving are as follows:
Wherein, αi>=0, i=1,2 ... m
Wherein, m is the quantity of the gradient direction noxkata feature, αi, αjMultiply for the Lagrangian number of the constraint solving
The factor, yi, yjFor the label of the positive negative sample, s.t is constraint condition.
In addition, to achieve the above object, the present invention also provides a kind of black eye intelligent judging device, which includes depositing
Reservoir and processor are stored with the black eye intelligent decision program that can be run on the processor in the memory, described
Black eye intelligent decision program realizes following steps when being executed by the processor:
Data receiver layer receive face image set, the face image set by tally set be divided into positive sample collection with
Negative sample collection, the positive sample collection and the negative sample collection are carried out include gray processing, binaryzation and noise reduction pretreatment operation
Afterwards, the positive sample collection of the pretreatment completion and negative sample collection and the tally set are input to data analysis layer；
Data analysis layer receives the positive sample collection that pretreatment is completed and negative sample collection and the tally set, and will be described
Tally set is input to black eye judgment models, and the data of the positive sample collection and negative sample collection are carried out direction gradient histogram fortune
The black eye judgment models are input to after calculation, the black eye judgment models are based on algorithm of support vector machine to the negative sample
This collection and the positive sample collection are trained, and are moved back when the loss function value in the algorithm of support vector machine is less than threshold value
It trains out；
Data in the test set are mapped to higher-dimension sky based on nonlinear mapping method by the test set for receiving user
Between, the black eye judgment models judgement is input to after the test set that the mapping is completed is carried out the operation of direction gradient histogram
Whether there is black eye, and exports result.
Optionally, the data in the positive sample collection be include black-eyed face, the data in the negative sample collection are
It does not include black-eyed face.
Optionally, the noise reduction process uses self-adapting image denoising filter method:
G (x, y)=η (x, y)+f (x, y)
Wherein, (x, y) indicates that image slices vegetarian refreshments coordinate, f (x, y) are based on the self-adapting image denoising filter method pair
The positive sample collection and the negative sample collection carry out the output data after noise reduction process, and η (x, y) is noise, and g (x, y) is described
Positive sample collection and the negative sample collection,For the noise population variance of the positive sample collection and the negative sample collection,It is described
The pixel grey scale mean value of (x, y),For the pixel grey scale variance of (x, y), L indicates current pixel point coordinate.
Optionally, black to being input to after the data progress direction gradient histogram operation of the positive sample collection and negative sample collection
Eyelet judgment models, comprising:
Calculate the gradient magnitude and gradient direction value of each pixel (x, y) of data in the face image set, and by institute
Gradient magnitude is stated as the first component, the gradient direction value forms gradient matrix as second component；
Data in the gradient matrix are divided into multiple fritters, and are added the gradient magnitude and gradient direction of each fritter
Value obtains additive value, and the additive value connected to form gradient direction noxkata feature and be input to black eye judgment models.
Optionally, the algorithm of support vector machine includes Nonlinear Mapping and constraint solving；
The Nonlinear Mapping are as follows:
κ(xi, xj)=< θ (xi), θ (xj)>
Wherein, < θ (xi), θ (xj) > indicate the gradient direction noxkata feature (xi, xj) Nonlinear Mapping inner product meter
It calculates, κ (xi, xj) it is the gradient direction noxkata feature (xi, xj) nonlinear mapping function；
The constraint solving are as follows:
Wherein, αi>=0, i=1,2 ... m
Wherein, m is the quantity of the gradient direction noxkata feature, αi, αjMultiply for the Lagrangian number of the constraint solving
The factor, yi, yjFor the label of the positive negative sample, s.t is constraint condition.
In addition, to achieve the above object, the present invention also provides a kind of computer readable storage medium, the computer can
It reads to be stored with black eye intelligent decision program on storage medium, the black eye intelligent decision program can be by one or more
Processor executes, the step of to realize black eye intelligent determination method as described above.
Black eye intelligent determination method, device and computer readable storage medium proposed by the present invention, data receiver layer connect
Face image set is received, the face image set is divided into positive sample collection and negative sample collection by tally set, by the positive sample
Collection and negative sample collection and the tally set are input to data analysis layer；Data analysis layer receives the positive sample that pretreatment is completed
Collection and negative sample collection and the tally set, and the tally set is input to black eye judgment models, by the positive sample collection
The black eye judgment models are input to after carrying out the operation of direction gradient histogram with the data of negative sample collection, the black eye is sentenced
Disconnected model is based on algorithm of support vector machine and is trained to the negative sample collection and the positive sample collection, until it is described support to
Loss function value in amount machine algorithm exits training when being less than threshold value；The test set of user is received, Nonlinear Mapping side is based on
Data in the test set are mapped to higher dimensional space by method, and the test set that the mapping is completed is carried out direction gradient histogram
It is input to the black eye judgment models after operation and judges whether there is black eye, and exports result.Due to service efficiency of the present invention
Higher supporting vector machine model, and early period reduces the noise for influencing model judgement based on a variety of data preprocessing methods, therefore
Accurately black eye intelligent judgment function may be implemented in the present invention.
Detailed description of the invention
Fig. 1 is the flow diagram for the black eye intelligent determination method that one embodiment of the invention provides；
Fig. 2 is the schematic diagram of internal structure for the black eye intelligent judging device that one embodiment of the invention provides；
The mould of black eye intelligent decision program in the black eye intelligent judging device that Fig. 3 provides for one embodiment of the invention
Block schematic diagram.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific embodiment
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to limit this hair
It is bright.
The present invention provides a kind of black eye intelligent determination method.Shown in referring to Fig.1, provided for one embodiment of the invention
The flow diagram of black eye intelligent determination method.This method can be executed by device, the device can by software and/
Or hardware realization.
In the present embodiment, black eye intelligent determination method includes:
S1, data receiver layer receive face image set, and the face image set is divided into positive sample collection by tally set
With negative sample collection, to the positive sample collection and the negative sample collection carry out include gray processing, binaryzation and noise reduction pretreatment grasp
After work, the positive sample collection of the pretreatment completion and negative sample collection and the tally set are input to data analysis layer.
Data in positive sample collection described in present pre-ferred embodiments be include black-eyed face, the negative sample collection
Interior data be do not include black-eyed face.
In present pre-ferred embodiments, gray processing operation is using rule of three by the positive sample collection and described negative
Data in sample switch to black-white-gray format from rgb format.The rule of three is as follows: obtaining the positive sample collection and described negative
The pixel is switched to black-white-gray format according to such as minor function by R, G, B pixel value of each pixel in sample:
0.30*R+0.59*G+0.11*B。
The binarization operation includes first given threshold, when the pixel in the black-white-gray format is greater than the threshold value,
The pixel becomes 255, and when the pixel in the black-white-gray format is less than the threshold value, the pixel becomes 0, i.e., described
Black and white format indicates that the pixel value of the positive sample collection and the negative sample collection is 0 or 255.
The noise reduction is to carry out noise reduction process, institute to the black and white formatted data based on self-adapting image denoising filter method
State self-adapting image denoising filter method are as follows:
G (x, y)=η (x, y)+f (x, y)
Wherein, (x, y) indicates that image slices vegetarian refreshments coordinate, f (x, y) are based on self-adapting image denoising filter method to described
Black and white formatted data carries out the output data after noise reduction process, and η (x, y) is noise, and g (x, y) is the black and white formatted data,For the noise population variance of the black and white formatted data,For the pixel grey scale mean value of (x, y),For (x, y)
Pixel grey scale variance, L indicate current pixel point coordinate.
S2, data analysis layer receive the positive sample collection and negative sample collection and the tally set that pretreatment is completed, and by institute
It states tally set and is input to black eye judgment models, the data of the positive sample collection and negative sample collection are subjected to direction gradient histogram
The black eye judgment models are input to after operation, the black eye judgment models are based on algorithm of support vector machine to described negative
Sample set and the positive sample collection are trained, when the loss function value in the algorithm of support vector machine is less than threshold value
Exit training.
In present pre-ferred embodiments, the data of the positive sample collection and negative sample collection carry out the operation of direction gradient histogram
After be input to black eye judgment models, comprising: calculate the gradient width of each pixel (x, y) of data in the face image set
Value and gradient direction value, and using the gradient magnitude as the first component, the gradient direction value forms ladder as second component
Spend matrix；Data in the gradient matrix are divided into multiple fritters, and calculate the gradient magnitude and gradient direction value of each fritter
And value, and described and value connected and forms gradient direction noxkata feature and is input to black eye judgment models.
Algorithm of support vector machine described in present pre-ferred embodiments includes Nonlinear Mapping and constraint solving；
The Nonlinear Mapping are as follows:
κ(xi, xj)=< θ (xi), θ (xj) >
Wherein, < θ (xi), θ (xj) > indicate the gradient direction noxkata feature (xi, xj) Nonlinear Mapping inner product meter
It calculates, κ (xi, xj) it is the gradient direction noxkata feature (xi, xj) nonlinear mapping function；
The constraint solving are as follows:
Wherein, αi>=0, i=1,2 ... m
Wherein, m is the quantity of the gradient direction noxkata feature, αi, αjMultiply for the Lagrangian number of the constraint solving
The factor, yi, yjFor the label of the positive negative sample, s.t is constraint condition.
Loss function described in present pre-ferred embodiments is least square method, and the loss function value is L (e):
Wherein, e is the trained values of the black eye judgment models and the error amount of the tally set, and k is the positive sample
The total quantity of collection and the negative sample collection, yiFor the tally set, y 'iFor the trained values, the threshold value is traditionally arranged to be
0.01。
Data in the test set are mapped to higher-dimension based on nonlinear mapping method by S3, the test set for receiving user
Space is sentenced the black eye judgment models are input to after the test set progress direction gradient histogram operation of the mapping completion
It is disconnected whether to have black eye, and export result.
The mapping of data described in present pre-ferred embodiments uses the nonlinear mapping method of the support vector machines.
Invention also provides a kind of black eye intelligent judging device.Referring to shown in Fig. 2, provided for one embodiment of the invention
The schematic diagram of internal structure of black eye intelligent judging device.
In the present embodiment, the black eye intelligent judging device 1 can be PC (Personal Computer, individual
Computer) or terminal devices such as smart phone, tablet computer, portable computer, it is also possible to a kind of server etc..This is black
Eyelet intelligent judging device 1 includes at least memory 11, processor 12, communication bus 13 and network interface 14.
Wherein, memory 11 includes at least a type of readable storage medium storing program for executing, and the readable storage medium storing program for executing includes dodging
It deposits, hard disk, multimedia card, card-type memory (for example, SD or DX memory etc.), magnetic storage, disk, CD etc..Storage
Device 11 can be the internal storage unit of black eye intelligent judging device 1 in some embodiments, such as the black eye is intelligently sentenced
The hard disk of disconnected device 1.Memory 11 is also possible to the external storage of black eye intelligent judging device 1 in further embodiments
The plug-in type hard disk being equipped in equipment, such as black eye intelligent judging device 1, intelligent memory card (Smart Media Card,
SMC), secure digital (Secure Digital, SD) blocks, flash card (Flash Card) etc..Further, memory 11 is gone back
Can both including black eye intelligent judging device 1 internal storage unit and also including External memory equipment.Memory 11 not only may be used
It is installed on the application software and Various types of data of black eye intelligent judging device 1, such as black eye intelligent decision journey for storage
The code etc. of sequence 01 can be also used for temporarily storing the data that has exported or will export.
Processor 12 can be in some embodiments a central processing unit (Central Processing Unit,
CPU), controller, microcontroller, microprocessor or other data processing chips, the program for being stored in run memory 11
Code or processing data, such as execute black eye intelligent decision program 01 etc..
Communication bus 13 is for realizing the connection communication between these components.
Network interface 14 optionally may include standard wireline interface and wireless interface (such as WI-FI interface), usually use
It is communicated to connect in being established between the device 1 and other electronic equipments.
Optionally, which can also include user interface, and user interface may include display (Display), defeated
Enter unit such as keyboard (Keyboard), optional user interface can also include standard wireline interface and wireless interface.It can
Selection of land, in some embodiments, display can be light-emitting diode display, liquid crystal display, touch-control liquid crystal display and
OLED (Organic Light-Emitting Diode, Organic Light Emitting Diode) touches device etc..Wherein, display can also fit
When be known as display screen or display unit, for being shown in the information handled in black eye intelligent judging device 1 and for showing
Show visual user interface.
Fig. 2 illustrates only the black eye intelligent judging device with component 11-14 and black eye intelligent decision program 01
1, it will be appreciated by persons skilled in the art that structure shown in fig. 1 does not constitute the limit to black eye intelligent judging device 1
It is fixed, it may include perhaps combining certain components or different component layouts than illustrating less perhaps more components.
In 1 embodiment of device shown in Fig. 2, black eye intelligent decision program 01 is stored in memory 11；Processor
Following steps are realized when the black eye intelligent decision program 01 stored in 12 execution memories 11:
Step 1: data receiver layer receives face image set, the face image set is divided the sample that is positive by tally set
This collection and negative sample collection, the positive sample collection and the negative sample collection are carried out include gray processing, binaryzation and noise reduction pre- place
After reason operation, the positive sample collection of the pretreatment completion and negative sample collection and the tally set are input to data analysis layer.
Data in positive sample collection described in present pre-ferred embodiments be include black-eyed face, the negative sample collection
Interior data be do not include black-eyed face.
In present pre-ferred embodiments, gray processing operation is using rule of three by the positive sample collection and described negative
Data in sample switch to black-white-gray format from rgb format.The rule of three is as follows: obtaining the positive sample collection and described negative
The pixel is switched to black-white-gray format according to such as minor function by R, G, B pixel value of each pixel in sample:
0.30*R+0.59*G+0.11*B
The binarization operation includes first given threshold, when the pixel in the black-white-gray format is greater than the threshold value,
The pixel becomes 255, and when the pixel in the black-white-gray format is less than the threshold value, the pixel becomes 0, i.e., it is described
Black and white format indicates that the pixel value of the positive sample collection and the negative sample collection is 0 or 255.
The noise reduction is to carry out noise reduction process, institute to the black and white formatted data based on self-adapting image denoising filter method
State self-adapting image denoising filter method are as follows:
G (x, y)=η (x, y)+f (x, y)
Wherein, (x, y) indicates that image slices vegetarian refreshments coordinate, f (x, y) are based on self-adapting image denoising filter method to described
Black and white formatted data carries out the output data after noise reduction process, and η (x, y) is noise, and g (x, y) is the black and white formatted data,For the noise population variance of the black and white formatted data,For the pixel grey scale mean value of (x, y),For (x, y)
Pixel grey scale variance, L indicate current pixel point coordinate.
Step 2: data analysis layer receives the positive sample collection that pretreatment is completed and negative sample collection and the tally set, and
The tally set is input to black eye judgment models, the data of the positive sample collection and negative sample collection are subjected to direction gradient
The black eye judgment models are input to after histogram operation, the black eye judgment models are based on algorithm of support vector machine to institute
It states negative sample collection and the positive sample collection is trained, until the loss function value in the algorithm of support vector machine is less than threshold
Training is exited when value.
In present pre-ferred embodiments, the data of the positive sample collection and negative sample collection carry out the operation of direction gradient histogram
After be input to black eye judgment models, comprising: calculate the gradient width of each pixel (x, y) of data in the face image set
Value and gradient direction value, and using the gradient magnitude as the first component, the gradient direction value forms ladder as second component
Spend matrix；Data in the gradient matrix are divided into multiple fritters, and calculate the gradient magnitude and gradient direction value of each fritter
And value, and described and value connected and forms gradient direction noxkata feature and is input to black eye judgment models.
Algorithm of support vector machine described in present pre-ferred embodiments includes Nonlinear Mapping and constraint solving；
The Nonlinear Mapping are as follows:
κ(xi, xj)=< θ (xi), θ (xj)>
Wherein, < θ (xi), θ (xj) > indicate the gradient direction noxkata feature (xi, xj) Nonlinear Mapping inner product meter
It calculates, κ (xi, xj) it is the gradient direction noxkata feature (xi, xj) nonlinear mapping function；
The constraint solving are as follows:
Wherein, αi>=0, i=1,2 ... m
Wherein, m is the quantity of the gradient direction noxkata feature, αi, αjMultiply for the Lagrangian number of the constraint solving
The factor, yi, yjFor the label of the positive negative sample, s.t is constraint condition.
Loss function described in present pre-ferred embodiments is least square method, and the loss function value is L (e):
Wherein, e is the trained values of the black eye judgment models and the error amount of the tally set, and k is the positive sample
The total quantity of collection and the negative sample collection, yiFor the tally set, y 'iFor the trained values, the threshold value is traditionally arranged to be
0.01。
Step 3: receiving the test set of user, the data in the test set are mapped to based on nonlinear mapping method
Higher dimensional space judges mould for the black eye is input to after the test set progress direction gradient histogram operation of the mapping completion
Type judges whether there is black eye, and exports result.
The mapping of data described in present pre-ferred embodiments uses the nonlinear mapping method of the support vector machines.
Optionally, in other embodiments, black eye intelligent decision program can also be divided into one or more mould
Block, one or more module are stored in memory 11, and (the present embodiment is processor by one or more processors
12) performed to complete the present invention, the so-called module of the present invention is the series of computation machine program for referring to complete specific function
Instruction segment, for describing implementation procedure of the black eye intelligent decision program in black eye intelligent judging device.
For example, referring to shown in Fig. 3, intelligently sentence for the black eye in one embodiment of black eye intelligent judging device of the present invention
The program module schematic diagram of disconnected program, in the embodiment, the black eye intelligent decision program can be divided into data receiver
Module 10, support vector machines training module 20, black eye judgment module 30 be illustratively:
The data reception module 10 is used for: receiving face image set, the face image set is divided by tally set
Be positive sample set and negative sample collection, carries out including gray processing, binaryzation and noise reduction to the positive sample collection and the negative sample collection
Pretreatment operation after, positive sample collection and negative sample collection and the tally set that the pretreatment is completed are input to data
Process layer.
The support vector machines training module 20 is used for: receive pretreatment complete positive sample collection and negative sample collection and
The tally set, and the tally set is input to black eye judgment models, by the data of the positive sample collection and negative sample collection
Be input to the black eye judgment models after carrying out the operation of direction gradient histogram, the black eye judgment models be based on supporting to
Amount machine algorithm is trained the negative sample collection and the positive sample collection, the loss in the algorithm of support vector machine
Functional value exits training when being less than threshold value.
The black eye judgment module 30 is used for: being received the test set of user, is based on nonlinear mapping method for the survey
Data in examination collection map to higher dimensional space, input after the test set that the mapping is completed is carried out the operation of direction gradient histogram
Black eye is judged whether there is to the black eye judgment models, and exports result.
The program modules quilts such as above-mentioned data reception module 10, support vector machines training module 20, black eye judgment module 30
Functions or operations step and the above-described embodiment realized when execution are substantially the same, and details are not described herein.
In addition, the embodiment of the present invention also proposes a kind of computer readable storage medium, the computer readable storage medium
On be stored with black eye intelligent decision program, the black eye intelligent decision program can be executed by one or more processors, with
Realize following operation:
Face image set is received, the face image set is divided into positive sample collection and negative sample collection by tally set, right
The positive sample collection and the negative sample collection carry out include gray processing, binaryzation and noise reduction pretreatment operation after, will be described pre-
It handles the positive sample collection completed and negative sample collection and the tally set is input to data analysis layer；
The positive sample collection and negative sample collection and the tally set that pretreatment is completed are received, and the tally set is inputted
To black eye judgment models, it is input to after the data of the positive sample collection and negative sample collection are carried out the operation of direction gradient histogram
The black eye judgment models, the black eye judgment models are based on algorithm of support vector machine to the negative sample collection and described
Positive sample collection is trained, and exits training when the loss function value in the algorithm of support vector machine is less than threshold value；
Data in the test set are mapped to higher-dimension sky based on nonlinear mapping method by the test set for receiving user
Between, the black eye judgment models judgement is input to after the test set that the mapping is completed is carried out the operation of direction gradient histogram
Whether there is black eye, and exports result.
Computer readable storage medium specific embodiment of the present invention and above-mentioned black eye intelligent judging device and method are each
Embodiment is essentially identical, does not make tired state herein.
It should be noted that the serial number of the above embodiments of the invention is only for description, do not represent the advantages or disadvantages of the embodiments.And
And the terms "include", "comprise" herein or any other variant thereof is intended to cover non-exclusive inclusion, so that
Process, device, article or method including a series of elements not only include those elements, but also including not arranging clearly
Other element out, or further include for this process, device, article or the intrinsic element of method.Not more
In the case where limitation, the element that is limited by sentence "including a ...", it is not excluded that include the element process, device,
There is also other identical elements in article or method.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but many situations
It is lower the former be more preferably embodiment.Based on this understanding, technical solution of the present invention is substantially in other words to the prior art
The part to contribute can be embodied in the form of software products, which is stored in as described above
In one storage medium (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that a terminal device (can be
Mobile phone, computer, server or network equipment etc.) execute method described in each embodiment of the present invention.
The above is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content, it is relevant to be applied directly or indirectly in other
Technical field is included within the scope of the present invention.
Claims (10)
1. a kind of black eye intelligent determination method, which is characterized in that the described method includes:
Data receiver layer receives face image set, and the face image set is divided into positive sample collection and negative sample by tally set
Collection, the positive sample collection and the negative sample collection are carried out include gray processing, binaryzation and noise reduction pretreatment operation after, by institute
It states the positive sample collection for pre-processing completion and negative sample collection and the tally set is input to data analysis layer；
Data analysis layer receives the positive sample collection and negative sample collection and the tally set that pretreatment is completed, and by the tally set
Black eye judgment models are input to, are inputted after the data of the positive sample collection and negative sample collection are carried out the operation of direction gradient histogram
To the black eye judgment models, the black eye judgment models are based on algorithm of support vector machine to the negative sample collection and described
Positive sample collection is trained, and exits training when the loss function value in the algorithm of support vector machine is less than threshold value；
Data in the test set are mapped to higher dimensional space based on nonlinear mapping method by the test set for receiving user, will
The black eye judgment models are input to after the test set progress direction gradient histogram operation that the mapping is completed to judge whether there is
Black eye, and export result.
2. black eye intelligent determination method as described in claim 1, which is characterized in that the data in the positive sample collection are packets
Include black-eyed face, the data in the negative sample collection be do not include black-eyed face.
3. black eye intelligent determination method as claimed in claim 1 or 2, which is characterized in that the noise reduction process is using adaptive
Answer image noise reduction filter method:
G (x, y)=η (x, y)+f (x, y)
Wherein, (x, y) indicate image slices vegetarian refreshments coordinate, f (x, y) be based on the self-adapting image denoising filter method to it is described just
Sample set and the negative sample collection carry out the output data after noise reduction process, and η (x, y) is noise, and g (x, y) is the positive sample
Collection and the negative sample collection,For the noise population variance of the positive sample collection and the negative sample collection,For (x, y)
Pixel grey scale mean value,For the pixel grey scale variance of (x, y), L indicates current pixel point coordinate.
4. such as the black eye intelligent determination method in claim 3, which is characterized in that the positive sample collection and negative sample collection
Data are input to black eye judgment models after carrying out the operation of direction gradient histogram, comprising:
Calculate the gradient magnitude and gradient direction value of each pixel (x, y) of data in the face image set, and by the ladder
Amplitude is spent as the first component, and the gradient direction value forms gradient matrix as second component；
Data in the gradient matrix are divided into multiple fritters, and are added the gradient magnitude of each fritter and gradient direction value obtains
Additive value, and the additive value connected to form gradient direction noxkata feature and be input to black eye judgment models.
5. black eye intelligent determination method as described in claim 1, which is characterized in that the algorithm of support vector machine includes non-
Linear Mapping and constraint solving；
The Nonlinear Mapping are as follows:
κ(xI,xj)=< θ (xi), θ (xj)>
Wherein, < θ (xi), θ (xj) > indicate the gradient direction noxkata feature (xi, xj) Nonlinear Mapping inner product calculate, κ (xi,
xj) it is the gradient direction noxkata feature (xi, xj) nonlinear mapping function；
The constraint solving are as follows:
Wherein, αi>=0, i=1,2 ... m
Wherein, m is the quantity of the gradient direction noxkata feature, αi, αjMultiply the factor for the Lagrangian number of the constraint solving,
yi, yjFor the label of the positive negative sample, s.t is constraint condition.
6. a kind of black eye intelligent judging device, which is characterized in that described device includes memory and processor, the memory
On be stored with the black eye intelligent decision program that can be run on the processor, the black eye intelligent decision program is described
Processor realizes following steps when executing:
Data receiver layer receives face image set, and the face image set is divided into positive sample collection and negative sample by tally set
Collection, the positive sample collection and the negative sample collection are carried out include gray processing, binaryzation and noise reduction pretreatment operation after, by institute
It states the positive sample collection for pre-processing completion and negative sample collection and the tally set is input to data analysis layer；
Data analysis layer receives the positive sample collection and negative sample collection and the tally set that pretreatment is completed, and by the tally set
Black eye judgment models are input to, are inputted after the data of the positive sample collection and negative sample collection are carried out the operation of direction gradient histogram
To the black eye judgment models, the black eye judgment models are based on algorithm of support vector machine to the negative sample collection and described
Positive sample collection is trained, and exits training when the loss function value in the algorithm of support vector machine is less than threshold value；
Data in the test set are mapped to higher dimensional space based on nonlinear mapping method by the test set for receiving user, will
The black eye judgment models are input to after the test set progress direction gradient histogram operation that the mapping is completed to judge whether there is
Black eye, and export result.
7. black eye intelligent judging device as described in claim 1, which is characterized in that the noise reduction process is using adaptive figure
As noise reduction filtering method:
G (x, y)=η (x, y)+f (x, y)
Wherein, (x, y) indicate image slices vegetarian refreshments coordinate, f (x, y) be based on the self-adapting image denoising filter method to it is described just
Sample set and the negative sample collection carry out the output data after noise reduction process, and η (x, y) is noise, and g (x, y) is the positive sample
Collection and the negative sample collection,For the noise population variance of the positive sample collection and the negative sample collection,For (x, y)
Pixel grey scale mean value,For the pixel grey scale variance of (x, y), L indicates current pixel point coordinate.
8. black eye intelligent judging device as claimed in claim 1 or 2, which is characterized in that the positive sample collection and negative sample
The data of this collection are input to black eye judgment models after carrying out the operation of direction gradient histogram, comprising:
Calculate the gradient magnitude and gradient direction value of each pixel (x, y) of data in the face image set, and by the ladder
Amplitude is spent as the first component, and the gradient direction value forms gradient matrix as second component；
Data in the gradient matrix are divided into multiple fritters, and are added the gradient magnitude of each fritter and gradient direction value obtains
Additive value, and the additive value connected to form gradient direction noxkata feature and be input to black eye judgment models.
9. black eye intelligent judging device as described in claim 1, which is characterized in that the algorithm of support vector machine includes non-
Linear Mapping and constraint solving；
The Nonlinear Mapping are as follows:
κ(xi, xj)=< θ (xi), θ (xj)>
Wherein, < θ (xi), θ (xj) > indicate the gradient direction noxkata feature (xi, xj) Nonlinear Mapping inner product calculate, κ (xi,
xj) it is the gradient direction noxkata feature (xi, xj) nonlinear mapping function；
The constraint solving are as follows:
Wherein, αi>=0, i=1,2 ... m
Wherein, m is the quantity of the gradient direction noxkata feature, αi, αjMultiply the factor for the Lagrangian number of the constraint solving,
yi, yjFor the label of the positive negative sample, s.t is constraint condition.
10. a kind of computer readable storage medium, which is characterized in that be stored with black eye on the computer readable storage medium
Intelligent decision program, the black eye intelligent decision program can be executed by one or more processor, to realize as right is wanted
Described in asking any one of 1 to 5 the step of black eye intelligent determination method.
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