CN108280388A - The method and apparatus and type of face detection method and device of training face detection model - Google Patents
The method and apparatus and type of face detection method and device of training face detection model Download PDFInfo
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- CN108280388A CN108280388A CN201710010709.3A CN201710010709A CN108280388A CN 108280388 A CN108280388 A CN 108280388A CN 201710010709 A CN201710010709 A CN 201710010709A CN 108280388 A CN108280388 A CN 108280388A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
- G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
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- 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
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
Abstract
The method and apparatus and type of face detection method and device for disclosing a kind of trained face detection model, wherein the method for training face detection model includes：At least two region recognition models are trained, each region recognition model in at least two region recognitions model is associated with a different parts of face, may belong to the region at position associated with the region recognition model in input picture for identification.In accordance with an embodiment of the present disclosure, face detection can rapidly and accurately be carried out.
Description
Technical field
This disclosure relates to image processing field, and in particular to can rapidly and accurately carry out the training face of face detection
The method and apparatus and type of face detection method and device of detection model.
Background technology
Face detection technique is one of important research content of computer vision and the skills such as face recognition, face tracking
One of committed step of art.It is all achieved in view of convolutional neural networks (CNN) when solving the problems, such as that computer vision field is multiple
Good effect, in recent years, people are attempted to carry out face detection using CNN models.Face detection based on CNN is broadly divided into two
A step：First, extracting, there may be the candidate regions of face；Second is that judging that the candidate region of face whether there is face using CNN
Portion.Fig. 1 shows the Structure and Process example that Face datection is carried out based on convolutional neural networks.As shown in Figure 1, based on CNN into
In the algorithm of row Face datection, face candidate region is usually firstly generated；Original image is input to convolutional layer again, generates input
The characteristic pattern of image；The corresponding feature graph region in face candidate region is input to full articulamentum, and then judges the candidate region
With the presence or absence of face, to generate Face datection result.The generating mode in face candidate region is mainly used in original graph at present
As it is upper or on characteristic pattern corresponding with original image sliding window, then judge whether the window is face location candidate regions
Domain.Fig. 2 is to show that in the prior art sliding window judges the exemplary figure of face location candidate region on the original image.Such as
Shown in Fig. 2, original image is divided into several image blocks, then judges whether each image block is face location candidate regions successively
Domain.Fig. 3 is to show that sliding window judges face location candidate regions on the characteristic pattern corresponding with original image in the prior art
The exemplary figure in domain.As shown in figure 3, original image is input to convolutional network, on the characteristic pattern generated by convolutional network
Sliding window operation is carried out, to judge face candidate region.Both modes are time-consuming all longer, cause to generate face candidate area
The time in domain judges that candidate region whether there is face the time it takes much larger than CNN.
Invention content
The brief overview about the disclosure is given below, in order to provide the basic of some aspects about the disclosure
Understand.It is understood, however, that this general introduction is not the exhaustive general introduction about the disclosure.It is not intended to for determining
The critical component or pith of the disclosure, nor being intended to limit the scope of the present disclosure.Its purpose is only with letter
The form of change provides certain concepts about the disclosure, in this, as preamble in greater detail given later.
In view of problem above, purpose of this disclosure is to provide a kind of training that can rapidly and accurately carry out face detection
The method and apparatus and type of face detection method and device of face detection model.
According to the one side of the disclosure, a kind of method of trained face detection model is provided, including：Training at least two
Region recognition model, a different parts of each region recognition model and face in at least two region recognitions model
It is associated, it can be used for identifying the region that may belong to position associated with the region recognition model in input picture.
According to another aspect of the present disclosure, a kind of device of trained face detection model is provided, including：Training unit,
It is configured at least two region recognition models of training, each region recognition model in at least two region recognitions model
Associated with a different parts of face, can be used for identifying in input picture may belong to related to the region recognition model
The region at the position of connection.
According to the another aspect of the disclosure, a kind of type of face detection method is provided, including：To input picture application respectively with
The associated at least two presumptive areas identification model of a different parts of face, can so as to be identified from input picture
The region at position associated with each presumptive area identification model can be belonged to；According to about face structure priori and portion
The region of position identified, can obtain the candidate region of the face to be detected in input picture；And use detection model
With the presence or absence of face in candidate region to judge face to be detected, the area present in face to be detected whereby it can be detected that
Domain.
According to the other aspects of the disclosure, additionally provide for realizing the above-mentioned computer program according to disclosed method
Code and computer program product and thereon record have this for realizing the above-mentioned computer program according to disclosed method
The computer readable storage medium of code.
The other aspects of the embodiment of the present disclosure are provided in following specification part, wherein be described in detail for abundant
Ground discloses the preferred embodiment of the embodiment of the present disclosure, without applying restriction to it.
Description of the drawings
The disclosure can by reference to being better understood below in association with the detailed description given by attached drawing, wherein
Same or analogous reference numeral has been used in all the appended drawings to indicate same or similar component.The attached drawing is together under
The detailed description in face includes in the present specification and to form part of specification together, for the disclosure is further illustrated
Preferred embodiment and explain the disclosure principle and advantage.Wherein：
Fig. 1 shows the Structure and Process example that Face datection is carried out based on convolutional neural networks；
Fig. 2 is to show that in the prior art sliding window judges the exemplary of face location candidate region on the original image
Figure；
Fig. 3 be show in the prior art on characteristic pattern corresponding with original image sliding window come judge face location wait
The exemplary figure of favored area；
Fig. 4 is the flow of the flow example for the method for showing trained face detection model according to an embodiment of the present disclosure
Figure；
Fig. 5 is to show to be split facial according to the location information according to face key point of the embodiment of the present disclosure
The exemplary figure of mark；
Fig. 6 be show according to the embodiment of the present disclosure at least one layer of each region recognition model using it is multiple not
With the exemplary figure of the filter of size；
Fig. 7 is the frame of the functional configuration example for the device for showing trained face detection model according to an embodiment of the present disclosure
Figure；
Fig. 8 is the flow chart for the flow example for showing type of face detection method according to an embodiment of the present disclosure；
Fig. 9 is schematically shown according to the initial baseline region of the embodiment of the present disclosure and in the benchmark of different directions
The exemplary figure in region；
Figure 10 schematically shows the structure of the generation of the candidate region of the face to be detected according to the embodiment of the present disclosure
The example of flow；
Whether Figure 11 schematically shows in the candidate region for judging face to be detected according to the embodiment of the present disclosure and deposits
In the example of the Structure and Process of face；
Figure 12 is the block diagram for the functional configuration example for showing face detection means according to an embodiment of the present disclosure；And
Figure 13 is the example for being shown as the personal computer of adoptable information processing unit in embodiment of the disclosure
The block diagram of structure.
Specific implementation mode
The exemplary embodiment of the disclosure is described hereinafter in connection with attached drawing.For clarity and conciseness,
All features of actual implementation mode are not described in the description.It should be understood, however, that developing any this actual implementation
Much decisions specific to embodiment must be made during example, to realize the objectives of developer, for example, symbol
Restrictive condition those of related to system and business is closed, and these restrictive conditions may have with the difference of embodiment
Changed.In addition, it will also be appreciated that although development is likely to be extremely complex and time-consuming, to having benefited from the disclosure
For those skilled in the art of content, this development is only routine task.
Herein, it is also necessary to which explanation is a bit, in order to avoid having obscured the disclosure because of unnecessary details, in the accompanying drawings
It illustrate only with according to the closely related device structure of the scheme of the disclosure and/or processing step, and be omitted and the disclosure
The little other details of relationship.
According to the one side of the disclosure, it is proposed that a kind of method of trained face detection model, the training face detection mould
The method of type can train at least two region recognitions model associated with a different parts of face respectively, to utilize
Stating at least two region recognition models and identifying fast automaticly may belong to related to each region recognition model in input picture
The region at the position of connection.
It is described in detail below in conjunction with the accompanying drawings in accordance with an embodiment of the present disclosure.
First, it will describe to be shown according to the flow of the method 400 of the training face detection model of the embodiment of the present disclosure with reference to Fig. 4
Example.Fig. 4 is the flow chart of the flow example for the method 400 for showing trained face detection model according to an embodiment of the present disclosure.
As shown in figure 4, the method 400 of trained face detection model according to an embodiment of the present disclosure includes training step S402.
First, in training step S402, at least two region recognition models of training, at least two region recognitions mould
Each region recognition model in type is associated with a different parts of face, and can be used for identifying in input picture may belong to
Region in position associated with the region recognition model.
Face can be divided into different positions such as left eye, right eye, left cheek, right cheek, nose, mouth and chin
Deng.In training step S402, according to the method 400 of the training face detection model of the embodiment of the present disclosure can train respectively with
The associated at least two region recognitions model of said one different parts of face, to utilize at least two region recognition
Model quickly identifies the region that may belong to position associated with each region recognition model in input picture.
Preferably, each region recognition model can be respectively trained based on the tab area location information at the position of face.
I.e., it is possible to be split to each position of face by facial key point location information, and use the mark at each position of face
Each region recognition model is respectively trained in zone position information.
Specifically, selection exist facial key point position markup information (the key point number of mark can be 5,13,21,
68 etc.) face data seeks at least one square for including all specific position key points according to the location information of facial key point
Shape frame (including the tab area location information at a facial position in each rectangle frame), at least one rectangle frame can incite somebody to action
Face is divided into several parts, to complete the segmentation mark of facial.And it is possible to use the mark at each position of face
Each region recognition model is respectively trained in note zone position information.Include the tab area at a facial position with rectangle frame above
Location information is only example, and it includes the tab area location information at a facial position that can also use ellipse etc..For convenience,
It is described by taking rectangle frame as an example below.
Fig. 5 is to show to be split facial according to the location information according to face key point of the embodiment of the present disclosure
The exemplary figure of mark.As shown in figure 5, one co-exists in 68 facial key point position markup informations, it is crucial according to these faces
The location information of point, can obtain 7 rectangle frames, this 7 rectangle frames respectively include being located at left eye, right eye, left cheek, right face
The key point of cheek, nose, mouth and chin area.It is then possible to use the tab area location information in this 7 rectangle frames point
Each region recognition model that Xun Lian be in 7 region recognition models.
Preferably, each region recognition model is used to generate characteristic pattern according to input picture, and this feature figure can indicate defeated
Enter the possibility that the pixel in image belongs to position associated with the region recognition model.
Specifically, a region recognition model is respectively trained using the facial different parts of mark.Each region recognition mould
The input of type is a secondary position mark figure for including complete original image and corresponding facial.The wherein position of facial
Mark figure is a secondary and equal-sized bianry image of original image size, and corresponding face area is labeled as 1, face other
Position and non-face zone marker are 0.Each region recognition model will export a characteristic pattern, and the point in characteristic pattern indicates input
Pixel in image belongs to the possibility at position associated with the region recognition model.To each region recognition model training
When, in order to improve the precision of region recognition model, it is desirable that facial corresponding with the region recognition model is in characteristic pattern
Response of the response much larger than other facial positions or non-face.It (can be empirically determined it is possible to further given threshold
The threshold value) to carry out binarization operation to response diagram (i.e. characteristic pattern), to obtain the response region at facial each position.
That is, if the pixel in input picture belongs to position associated with the region recognition model, in this feature figure with the pixel
The point of corresponding position is labeled as 1, and if the pixel in input picture is not belonging to portion associated with the region recognition model
Position is then labeled as 0 in this feature figure with the point of the pixel corresponding position, then when to each region recognition model training,
It is required that the characteristic pattern and position mark figure that are generated are as similar as possible, the characteristic pattern generated in this way can accurately indicate that institute is right
The facial region answered.
Preferably, each region recognition model may include multiple layers, can be adopted at least one of multiple layers layer
With different size of multiple filters, and the response of multiple filters can be combined to obtained result as next
The input of layer, to improve the possibility that the pixel in characteristic pattern reflection input picture belongs to position associated with region recognition model
The accuracy of property.
Specifically, in order to improve the precision of region recognition model, each region recognition model may include multiple layers, more
Different size of multiple filters, such as the filtering of 1 × 1,3 × 3,5 × 5 sizes may be used at least one of a layer layer
Device.It illustrates and unrestricted, the response of multiple filters can be carried out to Polynomial combination, and the result of Polynomial combination is made
For next layer of input, to improve the accuracy that characteristic pattern reflects the possibility.It, can be with other than Polynomial combination
Other combinations are carried out to the response of multiple filters, are described again here.
Illustrate and it is unrestricted, each region recognition model can be full convolutional network.In addition, each region recognition model is also
Can be other models other than full convolutional network.
Below for the convenience of description, describing to know in each region so that each region recognition model is full convolutional network as an example
Using the example of the filter of multiple and different sizes at least one layer of other model.Fig. 6 is shown according to the embodiment of the present disclosure
In at least one layer of each region recognition model using multiple and different sizes filter exemplary figure.In figure 6,
For the convenience of description, assume that the input of full convolutional network is that right cheek marks image, in the mark image, filled with black
Rectangle frame indicates the right cheek region of the face in original image.As shown in fig. 6, full convolutional network includes n convolutional layer, that is, roll up
Lamination 1, convolutional layer 2 ..., convolutional layer n, wherein n is integer more than 1.By taking convolutional layer 1 as an example, input is input to 3
The filter of a different size of filter i.e. 1 × 1,3 × 3,5 × 5, and the progress of the response results of this 3 filters is more
Item formula combination (α, β and γ in Fig. 6 are respectively weighting coefficient, and α, β and γ can be empirically determined respectively) is to obtain
It is exported to new response results, then using the output as the input of convolutional layer 2.As shown in fig. 6, the full convolutional network is defeated
It is the characteristic pattern after the corresponding binaryzation of original image to go out (i.e. the output of convolutional layer n), in the characteristic pattern after the binaryzation, with
The value of right cheek corresponding region is 1 (being indicated with black), and region corresponding with other positions in face and non-face is 0 (use
White indicates).From fig. 6 it can be seen that training the full convolutional network with the right cheek region of face so that this after training is complete
Convolutional network can identify the region that may belong to right cheek in input picture.
In order to further speed up the training speed of region recognition model, the part ginseng of different zones identification model can be shared
Number.It illustrates and unrestricted, in the case where each region recognition model is full convolutional network, different full convolutional networks can be shared
Identical layer parameter, for example, the preceding layer for the convolutional layer that full convolutional network includes or preceding two layers of parameter can be shared.
In conclusion the method 400 of trained face detection model according to an embodiment of the present disclosure need not be in original graph
Sliding window can identify and may belong in input picture and region recognition model phase on the characteristic pattern of picture or original image
The region at associated position, so as to carry out quick face detection.In addition, training face inspection according to an embodiment of the present disclosure
The each region recognition model surveyed in the method 400 of model is associated with a different parts of face, therefore can carry out more
Accurate face detection.
With the embodiment of the method for above-mentioned trained face detection model correspondingly, the disclosure additionally provide it is following training face
The embodiment of the device of detection model.
Fig. 7 is the functional configuration example for the device 700 for showing trained face detection model according to an embodiment of the present disclosure
Block diagram.
As shown in fig. 7, the device 700 of trained face detection model according to an embodiment of the present disclosure may include that training is single
Member 702.It is described below the functional configuration example of the unit.
In training unit 702, at least two region recognition models of training, in at least two region recognitions model
Each region recognition model is associated with a different parts of face, and can be used for identifying may belong to and be somebody's turn to do in input picture
The region at the associated position of region recognition model.
Face can be divided into different positions such as left eye, right eye, left cheek, right cheek, nose, mouth and chin
Deng.In training unit 702, according to the device 700 of the training face detection model of the embodiment of the present disclosure can train respectively with
The associated at least two region recognitions model of said one different parts of face, to utilize at least two region recognition
Model quickly identifies the region that may belong to position associated with each region recognition model in input picture.
Preferably, each region recognition model can be respectively trained based on the tab area location information at the position of face.
I.e., it is possible to be split to each position of face by facial key point location information, and use the mark at each position of face
Each region recognition model is respectively trained in zone position information.
Specifically, there is the face data of facial key point position markup information in selection, according to the position of facial key point
Information, it (includes one position of face in each rectangle frame to seek at least one rectangle frame comprising all specific position key points
Tab area location information), face can be divided into several parts by least one rectangle frame, to complete facial portion
The segmentation mark of position.And it is possible to which each region recognition is respectively trained using the tab area location information at each position of face
Model.
Based on the tab area location information at the position of face come the example of each region recognition model is respectively trained can be with
Referring to the description of corresponding position in above method embodiment, it is not repeated herein.
Preferably, each region recognition model is used to generate characteristic pattern according to input picture, and this feature figure can indicate defeated
Enter the possibility that the pixel in image belongs to position associated with the region recognition model.
The example that characteristic pattern is generated according to input picture may refer to the description of corresponding position in above method embodiment,
This is not repeated.
Preferably, each region recognition model may include multiple layers, can be adopted at least one of multiple layers layer
With different size of multiple filters, and the response of multiple filters can be combined to obtained result as next
The input of layer, to improve the possibility that the pixel in characteristic pattern reflection input picture belongs to position associated with region recognition model
The accuracy of property.
Specifically, in order to improve the precision of region recognition model, each region recognition model may include multiple layers, more
Different size of multiple filters, such as the filtering of 1 × 1,3 × 3,5 × 5 sizes may be used at least one of a layer layer
Device.It illustrates and unrestricted, the response of multiple filters can be carried out to Polynomial combination, and the result of Polynomial combination is made
For next layer of input, to improve the accuracy that characteristic pattern reflects the possibility.It, can be with other than Polynomial combination
Other combinations are carried out to the response of multiple filters, are described again here.
The example of filter at least one layer of each region recognition model using multiple and different sizes can join
The description for seeing corresponding position in above method embodiment, is not repeated herein.
Illustrate and it is unrestricted, each region recognition model can be full convolutional network.In addition, each region recognition model is also
Can be other models other than full convolutional network.
In order to further speed up the training speed of region recognition model, the part ginseng of different zones identification model can be shared
Number.It illustrates and unrestricted, in the case where each region recognition model is full convolutional network, different full convolutional networks can be shared
Identical layer parameter, for example, the preceding layer for the convolutional layer that full convolutional network includes or preceding two layers of parameter can be shared.
In conclusion the device 700 of trained face detection model according to an embodiment of the present disclosure need not be in original graph
Sliding window can identify and may belong in input picture and region recognition model phase on the characteristic pattern of picture or original image
The region at associated position, so as to carry out quick face detection.In addition, training face inspection according to an embodiment of the present disclosure
The each region recognition model surveyed in the device 700 of model is associated with a different parts of face, therefore can carry out more
Accurate face detection.
It is noted that although the foregoing describe the functions of the device of trained face detection model according to an embodiment of the present disclosure
Configuration, but this is only exemplary rather than limitation, and those skilled in the art can be according to the principle of the disclosure to above example
It modifies, such as the function module in each embodiment can be added, deleted or be combined, and such modification
It each falls in the scope of the present disclosure.
It is furthermore to be noted that device embodiment here is corresponding with above method embodiment, therefore in device reality
The description that the content not being described in detail in example can be found in corresponding position in embodiment of the method is applied, is not repeated to describe herein.
It should be understood that the instruction that the machine in storage medium and program product according to an embodiment of the present disclosure can perform may be used also
To be configured to the method for executing above-mentioned trained face detection model, the content that therefore not described in detail here can refer to previous phase
The description of position is answered, is not repeated to be described herein.
Correspondingly, the storage medium of the program product for carrying the above-mentioned instruction that can perform including machine is also included within this
In the disclosure of invention.The storage medium includes but not limited to floppy disk, CD, magneto-optic disk, storage card, memory stick etc..
According to another aspect of the present disclosure, a kind of type of face detection method is provided, which can quickly simultaneously
Accurately carry out face detection.
The flow example of type of face detection method 800 according to an embodiment of the present disclosure is described next, with reference to Fig. 8.Fig. 8 is
The flow chart of the flow example of type of face detection method 800 according to an embodiment of the present disclosure is shown.As shown in figure 8, according to this public affairs
The type of face detection method 800 for the embodiment opened includes identification region step S802, obtains candidate region step S804 and judgement
Step S806.
It, can be associated with a different parts of face respectively to input picture application in identification region step S802
At least two presumptive area identification models, so as to identified from input picture may belong to and each presumptive area identify
The region at the associated position of model.
It illustrates and unrestricted, at least two presumptive areas identification model can be using according to the embodiment of the present disclosure
The region recognition model that the method 400 of training face detection model obtains, more specifically, each presumptive area identification model can be with
It is the full convolutional network for utilizing the method 400 according to the training face detection model of the embodiment of the present disclosure to obtain.In addition, it is described extremely
Few two presumptive area identification models can also be the region recognition model obtained using other methods, as long as described at least two
Each presumptive area identification model in presumptive area identification model is associated with a different parts of face respectively and uses
In identifying the region that may belong to position associated with the presumptive area identification model in input picture.
Specifically, in identification region step S802, pass through each of described at least two presumptive areas identification model
Presumptive area identification model obtains characteristic pattern of the input picture under the presumptive area identification model, and (threshold value can for given threshold
With empirically determined) come to characteristic pattern carry out binarization operation, to obtain in face with the presumptive area identification model phase
The response region at associated position.According to the position correspondence of characteristic pattern and input picture, so that it may to obtain and these responses
Region in the corresponding input picture in region, i.e., position associated with the presumptive area identification model in the face of input picture
The band of position.
It can be seen that since at least two presumptive areas identification model is related to a different parts of face respectively
Connection, therefore can be from input picture using each presumptive area identification model in at least two presumptive areas identification model
The region at position associated with each presumptive area identification model may be belonged to by automatically identifying.
Preferably, the ginseng in the different zones identification model in at least two presumptive areas identification model can be shared
Number, to accelerate the speed of identification.Illustrate and it is unrestricted, in the case where each presumptive area identification model is full convolutional network,
The parameter that the identical layer of different full convolutional networks can be shared, for example, the convolutional layer that full convolutional network includes can be shared
Preceding layer or preceding two layers of parameter, to accelerate the speed of identification.
In obtain candidate region step S804, can according to about face structure priori and position identified
The region gone out obtains the candidate region of the face to be detected in input picture.
It specifically, can be according to related facial and facial overall region in obtaining candidate region step S804
It the priori of relative size and relative position relation and is identified using at least two presumptive areas identification model
Position region, estimate in input picture there may be face region face i.e. to be detected candidate region.
Preferably, the candidate region of the face to be detected in input picture can be obtained in the following manner：It can be based on
Relative size and the priori of relative position relation about facial and facial overall region and being identified for position
The relative position of face to be detected is estimated to obtain the initial baseline region of face to be detected, and can be incited somebody to action in the region gone out
Candidate region of the initial baseline region as face to be detected；And initial baseline region can be rotated successively predetermined angular with
The reference area in different directions is obtained, and can be by the reference area in different directions also as face to be detected
Candidate region.
It specifically, can be with for the region at a position of the face identified using each presumptive area identification model
Priori based on relative size and relative position relation about the facial and facial overall region and utilize institute
The region at the position identified estimates the relative position of face to be detected to obtain the initial baseline area of face to be detected
Domain；Since actual scene septum reset is likely to be present in all angles, which is rotated into special angle to obtain
Obtain the reference area in different directions.The initial baseline that the region at the position based on each face identified can be obtained
Region and candidate region of the reference area all as face to be detected in different directions, it is possible thereby to more accurately carry out face
It detects in portion.Wherein it is possible to rule of thumb determine above-mentioned predetermined angular.
Reference area is rotated into special angle to schematically show by taking a position of the face identified as an example below
Example.Fig. 9 is schematically shown according to the initial baseline region of the embodiment of the present disclosure and in the benchmark of different directions
The exemplary figure in region.Assuming that with the rectangle frame of filled black being the face identified using presumptive area identification model in a of Fig. 9
The region of the right cheek in portion.Based on about right cheek position and the facial relative size of overall region and the elder generation of relative position relation
The region at the right cheek position tested knowledge and identified, it is estimated that the relative position of face to be detected is to obtain
The reference area of face to be detected for the clearness of diagram, does not draw the accurate reference area in fig.9, but with
The right cheek region identified represents the reference area.Being respectively illustrated with the rectangle frame of filled black in b to the h of Fig. 9 will be right
Cheek region rotates the right cheek region in different directions obtained after predetermined angular (since right cheek region represents base successively
Quasi- region, therefore the right cheek region in different directions represents the reference area in different directions).
As described above, each presumptive area identification model can be full convolutional network.Figure 10 schematically shows basis
The example of the Structure and Process of the generation of the candidate region of the face to be detected of the embodiment of the present disclosure.In Fig. 10, in the first row most
The image in left side is the input picture for including face to be detected；Input picture is separately input to full convolutional network 1, full convolution net
Network 2 ... and full convolutional network n (wherein, n is the integer more than 1), input picture can be obtained in different full convolutional networks
Response results, that is, the characteristic response region that can obtain position associated with each full convolutional network in input picture is
Simplification, irregular figure shown in second image of the first row schematically shows six of face in Figure 10
Characteristic response region corresponding to position, that is, left eye, right eye, left cheek, right cheek, nose and chin；According to features described above
Position correspondence between response region and input picture can obtain the corresponding input picture region of these response regions i.e.
Region where the left eye of input picture septum reset, right eye, left cheek, right cheek, nose and chin, in Fig. 10 first
Schematically shown in third image in row the left eye of input picture septum reset, right eye, left cheek, right cheek, nose,
And the region where chin；In addition, being schematically shown in Fig. 10 based on obtained above-mentioned in the image of the second row
The reference area for the face to be detected that the region at position and priori about face structure obtain respectively, here for letter
It is single, the reference area in different directions is not shown；In addition, in order to intuitively show the candidate region of face to be detected,
Schematically the reference area of above-mentioned face to be detected is indicated together in input figure in the 4th image of the first row in Figure 10
As upper.
The mode of the candidate region of face to be detected in input picture achieved above is only example, can also pass through ability
Other technologies means in domain obtain the candidate region of the face to be detected in input picture, are described again here.
In judgment step S806, it can judge to whether there is in the candidate region of face to be detected using detection model
Face, to detect the region present in face to be detected.
Specifically, in judgment step S806, can judge obtaining candidate region step S804 using detection model
With the presence or absence of face in the candidate region of the face to be detected of middle acquisition, to detect the region present in face to be detected.
Illustrate and it is unrestricted, detection model can be convolutional neural networks.In addition, detection model can also be except convolution god
Through other models except network.
Preferably, in judgment step S806, can judge in the following manner be in the candidate region of face to be detected
It is no to there is face：Input picture is input to the convolutional layer of convolutional neural networks, generates the characteristic pattern of input picture；Will with it is to be checked
The region for surveying the corresponding characteristic pattern in candidate region of face is input to the full articulamentum of convolutional neural networks；And based on described defeated
The region for entering the characteristic pattern and characteristic pattern corresponding with the candidate region of face to be detected of image, judges the candidate of face to be detected
With the presence or absence of face in region.
Whether Figure 11 schematically shows in the candidate region for judging face to be detected according to the embodiment of the present disclosure and deposits
In the example of the Structure and Process of face.
In fig. 11, the image of the leftmost side is the input picture for including face to be detected in the second row, by the input picture
It is input to the convolutional layer of convolutional neural networks, generates the characteristic pattern of input picture.The diagram of the first row is substantially and Figure 10 in Figure 11
The first row diagram it is identical, be described again here.The region surrounded with white box in the third image of the first row in Figure 11
Schematically show the candidate region of face to be detected.By characteristic pattern corresponding with the above-mentioned candidate region of face to be detected
Region is input to the full articulamentum of convolutional neural networks；May then based on input picture characteristic pattern and with it is to be detected face
The region of the corresponding characteristic pattern in candidate region judges in the candidate region of face to be detected with the presence or absence of face, the third of Figure 11
The region surrounded with white box in capable image indicates the face that detected.
In conclusion type of face detection method 800 according to an embodiment of the present disclosure need not be in original image or original
Sliding window can filter out the candidate region of face on the characteristic pattern of image, so as to carry out quick face detection.This
Outside, each presumptive area identification model in type of face detection method 800 according to an embodiment of the present disclosure with face one not
It is associated with position, therefore more accurate face detection can be carried out.
With above-mentioned type of face detection method embodiment correspondingly, the disclosure additionally provides the implementation of following face detection means
Example.
Figure 12 is the block diagram for the functional configuration example for showing face detection means 1200 according to an embodiment of the present disclosure.
As shown in figure 12, face detection means 1200 according to an embodiment of the present disclosure may include identification region unit
1202, candidate region unit 1204 and judging unit 1206 are obtained.It is described below the functional configuration example of each unit.
It, can be associated with a different parts of face respectively to input picture application in identification region unit 1202
At least two presumptive area identification models, so as to identified from input picture may belong to and each presumptive area identify
The region at the associated position of model.
It illustrates and unrestricted, at least two presumptive areas identification model can be using according to the embodiment of the present disclosure
The region recognition model that the method 400 of training face detection model obtains, more specifically, each presumptive area identification model can be with
It is the full convolutional network for utilizing the method 400 according to the training face detection model of the embodiment of the present disclosure to obtain.In addition, it is described extremely
Few two presumptive area identification models can also be the region recognition model obtained using other methods, as long as described at least two
Each presumptive area identification model in presumptive area identification model is associated with a different parts of face respectively and uses
In identifying the region that may belong to position associated with the presumptive area identification model in input picture.
Specifically, in identification region unit 1202, pass through each of described at least two presumptive areas identification model
Presumptive area identification model obtains characteristic pattern of the input picture under the presumptive area identification model, and (threshold value can for given threshold
With empirically determined) come to characteristic pattern carry out binarization operation, to obtain in face with the presumptive area identification model phase
The response region at associated position.According to the position correspondence of characteristic pattern and input picture, so that it may to obtain and these responses
Region in the corresponding input picture in region, i.e., position associated with the presumptive area identification model in the face of input picture
The band of position.
It can be seen that since at least two presumptive areas identification model is related to a different parts of face respectively
Connection, therefore can be from input picture using each presumptive area identification model in at least two presumptive areas identification model
The region at position associated with each presumptive area identification model may be belonged to by automatically identifying.
Preferably, the ginseng in the different zones identification model in at least two presumptive areas identification model can be shared
Number, to accelerate the speed of identification.Illustrate and it is unrestricted, in the case where each presumptive area identification model is full convolutional network,
The parameter that the identical layer of different full convolutional networks can be shared, for example, the convolutional layer that full convolutional network includes can be shared
Preceding layer or preceding two layers of parameter, to accelerate the speed of identification.
In obtain candidate region unit 1204, can according to about face structure priori and position identified
The region gone out obtains the candidate region of the face to be detected in input picture.
It specifically, can be according to related facial and facial overall region in obtaining candidate region unit 1204
It the priori of relative size and relative position relation and is identified using at least two presumptive areas identification model
Position region, estimate in input picture there may be face region face i.e. to be detected candidate region.
Preferably, the candidate region of the face to be detected in input picture can be obtained in the following manner：It can be based on
Relative size and the priori of relative position relation about facial and facial overall region and being identified for position
The relative position of face to be detected is estimated to obtain the initial baseline region of face to be detected, and can be incited somebody to action in the region gone out
Candidate region of the initial baseline region as face to be detected；And initial baseline region can be rotated successively predetermined angular with
The reference area in different directions is obtained, and can be by the reference area in different directions also as face to be detected
Candidate region.
It specifically, can be with for the region at a position of the face identified using each presumptive area identification model
Priori based on relative size and relative position relation about the facial and facial overall region and utilize institute
The region at the position identified estimates the relative position of face to be detected to obtain the initial baseline area of face to be detected
Domain；Since actual scene septum reset is likely to be present in all angles, which is rotated into special angle to obtain
Obtain the reference area in different directions.The initial baseline that the region at the position based on each face identified can be obtained
Region and candidate region of the reference area all as face to be detected in different directions, it is possible thereby to more accurately carry out face
It detects in portion.Wherein it is possible to rule of thumb determine above-mentioned predetermined angular.
The example of the generation of the candidate region of face to be detected may refer to retouching for corresponding position in above method embodiment
It states, is not repeated herein.
In judging unit 1206, it can judge to whether there is in the candidate region of face to be detected using detection model
Face, to detect the region present in face to be detected.
Specifically, in judging unit 1206, can judge obtaining candidate region unit 1204 using detection model
With the presence or absence of face in the candidate region of the face to be detected of middle acquisition, to detect the region present in face to be detected.
Illustrate and it is unrestricted, detection model can be convolutional neural networks.In addition, detection model can also be except convolution god
Through other models except network.
Preferably, in judging unit 1206, can judge in the following manner be in the candidate region of face to be detected
It is no to there is face：Input picture is input to the convolutional layer of convolutional neural networks, generates the characteristic pattern of input picture；Will with it is to be checked
The region for surveying the corresponding characteristic pattern in candidate region of face is input to the full articulamentum of convolutional neural networks；And based on described defeated
The region for entering the characteristic pattern and characteristic pattern corresponding with the candidate region of face to be detected of image, judges the candidate of face to be detected
With the presence or absence of face in region.
Judge to may refer in above method embodiment with the presence or absence of the example of face in the candidate region of face to be detected
The description of corresponding position, is not repeated herein.
In conclusion face detection means 1200 according to an embodiment of the present disclosure need not be in original image or original
Sliding window can filter out the candidate region of face on the characteristic pattern of image, so as to carry out quick face detection.This
Outside, each presumptive area identification model in face detection means 1200 according to an embodiment of the present disclosure with face one not
It is associated with position, therefore more accurate face detection can be carried out.
It is noted that although the foregoing describe the functional configuration of face detection means according to an embodiment of the present disclosure,
This is only exemplary rather than limitation, and those skilled in the art can modify to above example according to the principle of the disclosure,
Such as the function module in each embodiment can be added, deleted or be combined, and such modification each falls within this
In scope of disclosure.
It is furthermore to be noted that device embodiment here is corresponding with above method embodiment, therefore in device reality
The description that the content not being described in detail in example can be found in corresponding position in embodiment of the method is applied, is not repeated to describe herein.
It should be understood that the instruction that the machine in storage medium and program product according to an embodiment of the present disclosure can perform may be used also
To be configured to execute above-mentioned type of face detection method, the content that therefore not described in detail here can refer to retouching for previous corresponding position
It states, is not repeated to be described herein.
Correspondingly, the storage medium of the program product for carrying the above-mentioned instruction that can perform including machine is also included within this
In the disclosure of invention.The storage medium includes but not limited to floppy disk, CD, magneto-optic disk, storage card, memory stick etc..
In addition, it should also be noted that above-mentioned series of processes and device can also be realized by software and/or firmware.
In the case of being realized by software and/or firmware, from storage medium or network to the computer with specialized hardware structure, such as
Shown in Figure 13 general purpose personal computer 1300 installation constitute the software program, the computer when being equipped with various programs,
It is able to carry out various functions etc..
In fig. 13, central processing unit (CPU) 1301 according to the program stored in read-only memory (ROM) 1302 or from
The program that storage section 1308 is loaded into random access memory (RAM) 1303 executes various processing.In RAM 1303, also root
The data required when CPU 1301 executes various processing etc. are stored according to needs.
CPU 1301, ROM 1302 and RAM 1303 are connected to each other via bus 1304.Input/output interface 1305 also connects
It is connected to bus 1304.
Components described below is connected to input/output interface 1305：Importation 1306, including keyboard, mouse etc.；Output par, c
1307, including display, such as cathode-ray tube (CRT), liquid crystal display (LCD) etc. and loud speaker etc.；Storage section
1308, including hard disk etc.；With communications portion 1309, including network interface card such as LAN card, modem etc..Communications portion
1309 execute communication process via network such as internet.
As needed, driver 1310 is also connected to input/output interface 1305.The such as disk of detachable media 1311,
CD, magneto-optic disk, semiconductor memory etc. are installed on driver 1310 as needed so that the computer read out
Program is mounted to as needed in storage section 1308.
It is such as removable from network such as internet or storage medium in the case of series of processes above-mentioned by software realization
Unload the program that the installation of medium 1311 constitutes software.
It will be understood by those of skill in the art that this storage medium is not limited to wherein be stored with journey shown in Figure 13
Sequence is separately distributed with equipment to provide a user the detachable media 1311 of program.The example packet of detachable media 1311
Containing disk (include floppy disk (registered trademark)), CD (including compact disc read-only memory (CD-ROM) and digital versatile disc (DVD)),
Magneto-optic disk (including mini-disk (MD) (registered trademark)) and semiconductor memory.Alternatively, storage medium can be ROM 1302, deposit
The hard disk etc. for including in storage part 1308, wherein computer program stored, and user is distributed to together with the equipment comprising them.
Preferred embodiment of the present disclosure is described above by reference to attached drawing, but the disclosure is certainly not limited to above example.This
Field technology personnel can obtain various changes and modifications within the scope of the appended claims, and should be understood that these changes and repair
Changing nature will fall into scope of the presently disclosed technology.
For example, can be realized in the embodiment above by the device separated including multiple functions in a unit.
As an alternative, the multiple functions of being realized in the embodiment above by multiple units can be realized by the device separated respectively.In addition, with
One of upper function can be realized by multiple units.Needless to say, such configuration includes in scope of the presently disclosed technology.
In this specification, described in flow chart the step of includes not only the place executed in temporal sequence with the sequence
Reason, and include concurrently or individually rather than the processing that must execute in temporal sequence.In addition, even in temporal sequence
In the step of processing, needless to say, the sequence can also be suitably changed.
In addition, can also be configured as follows according to the technology of the disclosure.
A kind of 1. methods of trained face detection model are attached, including：
Train at least two region recognition models, each region recognition model in at least two region recognitions model
It is associated with a different parts of face, it may belong to associated with the region recognition model in input picture for identification
The region at position.
The method of training face detection model of the note 2. according to note 1, wherein the mark at the position based on face
Zone position information is respectively trained each region recognition model.
The method of training face detection model of the note 3. according to note 1, wherein：
Each region recognition model is used to generate characteristic pattern according to input picture, and the characteristic pattern indicates in input picture
Pixel belongs to the possibility at position associated with the region recognition model.
It is attached 4. methods according to trained face detection model described in note 3, wherein each region recognition model includes
Multiple layers use different size of multiple filters at least one of the multiple layer layer, and by the multiple filter
The response of wave device is combined obtained result as next layer of input, reflects the possibility to improve the characteristic pattern
Accuracy.
The method of training face detection model of the note 5. according to note 1, wherein each region recognition model is complete
Convolutional network.
A kind of 6. devices of trained face detection model are attached, including：
Training unit is configured at least two region recognition models of training, in at least two region recognitions model
Each region recognition model with face a different parts it is associated, may belong in input picture for identification and the area
The region at the associated position of domain identification model.
The device of training face detection model of the note 7. according to note 6, wherein the mark at the position based on face
Zone position information is respectively trained each region recognition model.
The device of training face detection model of the note 8. according to note 6, wherein：
Each region recognition model is used to generate characteristic pattern according to input picture, and the characteristic pattern indicates in input picture
Pixel belongs to the possibility at position associated with the region recognition model.
It is attached 9. devices according to trained face detection model described in note 8, wherein each region recognition model includes
Multiple layers use different size of multiple filters at least one of the multiple layer layer, and by the multiple filter
The response of wave device is combined obtained result as next layer of input, reflects the possibility to improve the characteristic pattern
Accuracy.
The device of training face detection model of the note 10. according to note 6, wherein each region recognition model is
Full convolutional network.
A kind of 11. type of face detection method are attached, including：
To input picture application, at least two presumptive area associated with a different parts of face identifies mould respectively
Type, to identify the region that may belong to position associated with each presumptive area identification model from the input picture；
According to the region of priori and position about face structure identified, obtain in the input picture
The candidate region of face to be detected；And
Judged using detection model with the presence or absence of face in the candidate region of the face to be detected, to detect
State the region present in face to be detected.
Type of face detection method of the note 12. according to note 11, wherein obtain the input picture in the following manner
In face to be detected candidate region：
Priori based on relative size and relative position relation about facial and facial overall region and
The relative position of the face to be detected is estimated to obtain the initial of the face to be detected in the region identified at position
Reference area, and using the initial baseline region as the candidate region of the face to be detected；And
The initial baseline region is rotated into predetermined angular successively to obtain the reference area for being in different directions, and will
The reference area in the different directions also candidate region as the face to be detected.
Type of face detection method of the note 13. according to note 11, wherein the shared at least two presumptive areas identification
The parameter in different zones identification model in model, to accelerate the speed of identification.
Type of face detection method of the note 14. according to note 11, wherein the detection model is convolutional neural networks.
Type of face detection method of the note 15. according to note 14, wherein judge the face to be detected in the following manner
With the presence or absence of face in the candidate region in portion：
The input picture is input to the convolutional layer of the convolutional neural networks, generates the feature of the input picture
Figure；
The region of characteristic pattern corresponding with the candidate region of face to be detected is input to the convolutional neural networks
Full articulamentum；And
The area of characteristic pattern based on the input picture and characteristic pattern corresponding with the candidate region of face to be detected
Domain judges in the candidate region of the face to be detected with the presence or absence of face.
Claims (10)
1. a kind of method of trained face detection model, including：
Train at least two region recognition models, each region recognition model in at least two region recognitions model and face
One different parts in portion are associated, may belong to position associated with the region recognition model in input picture for identification
Region.
2. the method for trained face detection model according to claim 1, wherein the tab area at the position based on face
Location information is respectively trained each region recognition model.
3. the method for trained face detection model according to claim 1, wherein：
Each region recognition model is used to generate characteristic pattern according to input picture, and the characteristic pattern indicates the pixel in input picture
Belong to the possibility at position associated with the region recognition model.
4. the method for trained face detection model according to claim 3, wherein each region recognition model includes multiple
Layer uses different size of multiple filters at least one of the multiple layer layer, and by the multiple filter
Response be combined obtained result as next layer of input, to improve the standard that the characteristic pattern reflects the possibility
Exactness.
5. the method for trained face detection model according to claim 1, wherein each region recognition model is full convolution
Network.
6. a kind of device of trained face detection model, including：
Training unit is configured at least two region recognition models of training, every in at least two region recognitions model
A region recognition model is associated with a different parts of face, may belong in input picture know with the region for identification
The region at the other associated position of model.
7. a kind of type of face detection method, including：
At least two presumptive areas identification model associated with a different parts of face respectively is applied to input picture, from
And the region that may belong to position associated with each presumptive area identification model is identified from the input picture；
According to the region of priori and position about face structure identified, obtain to be checked in the input picture
Survey the candidate region of face；And
Judged using detection model with the presence or absence of face in the candidate region of the face to be detected, to detect described wait for
Region present in detection face.
8. type of face detection method according to claim 7, wherein obtain waited in the input picture in the following manner
Detect the candidate region of face：
Based on about facial and the facial relative size of overall region and priori and the position of relative position relation
The region identified, estimate the relative position of the face to be detected to obtain the initial baseline of the face to be detected
Region, and using the initial baseline region as the candidate region of the face to be detected；And
The initial baseline region is rotated into predetermined angular successively to obtain the reference area for being in different directions, and will be described
Reference area in the different directions also candidate region as the face to be detected.
9. type of face detection method according to claim 7, wherein share in at least two presumptive areas identification model
Different zones identification model in parameter, with accelerate identification speed.
10. type of face detection method according to claim 7, wherein the detection model is convolutional neural networks.
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