CN109063565A - A kind of low resolution face identification method and device - Google Patents
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Abstract
The present invention provides a kind of low resolution face identification method and device, for the recognition of face problem under low resolution, low resolution recognition of face problem especially under natural environment, using double-current deep learning network structure, pass through the efficient low resolution recognition of face network of selection sex knowledge distillation training, it realizes low resolution recognition of face, there is the precision close to high-resolution human face identification model, then there is great advantage in speed, memory.
Description
Technical field
The invention belongs to computer vision and deep learning field more particularly to a kind of people under the conditions of low resolution
Face recognition method and device.
Background technique
Face is as the essential attribute distinguished between individual, in computer vision and multimedia application field quilt daily
Frequently identification.In these applications, human face recognition model needs are redeployed in cell phone even intelligent video camera head, are used for
The numerous areas such as camera auto-focusing, human-computer interaction, photo management, city safety monitoring, intelligent driving.Currently, recognition of face
In practical application under the conditions of open environment, it is often necessary to identify low resolution under the conditions of extremely low calculating and memory source
Facial image.In this case, current many high-precision human face recognition models are often complex, it is difficult to actual deployment,
And after the reduction of facial image resolution ratio, accuracy of identification can also generate very big decline.A kind of effective scheme is
These high performance faceforms are converted into efficient model, and make the model after these conversions in identification low-resolution image
When be able to maintain the precision of original complex model.
In recent years, low resolution field of face identification also studied certain methods.Existing method is typically split into two classes, packet
Include the method based on enhancing and the method based on insertion.Method based on enhancing first carries out super-resolution to low-resolution face image
Rate enhancing is rebuild, and then the model of retraining higher resolution is identified, facial image of the such methods after identification is rebuild
Can have better as a result, still super-resolution operates and the high identification of rate respectively can bring biggish calculating cost, reduction
The processing speed of model, constrains the actual deployment of model.In contrast, the method based on insertion is directly to low resolution people
Face image extracts the feature with resolving ability, then training characteristics identifier progress recognition of face, most important in this method
Processing be by the Knowledge delivery of high-resolution human face to low resolution face.Nevertheless, a key problem in technology be how from
High-resolution human face correctly transmits desired knowledge rather than the A to Z of to low resolution face.This selective knowledge
Transmitting is most important when converting existing high-precision human face recognition model into efficient low resolution human face recognition model
One of challenge.
Summary of the invention
In order to overcome the deficiencies of the prior art, the present invention provides a kind of low resolution face identification method and device, the party
Method passes through choosing using double-current deep learning network structure (including teacher's flow depth degree learning network and student's flow depth degree learning network)
The efficient low resolution recognition of face network of sex knowledge distillation training is selected, the network implementations low resolution recognition of face is passed through.
In order to solve the above technical problems, the present invention is achieved through the following technical solutions:
A kind of low resolution face identification method, step include:
Using high-resolution human face image one deep neural network of training with identity marks, obtains teacher and flow deep learning
Network；
Utilize high-resolution human face image and low-resolution face image the training one depth nerve marked with common identity
Network obtains student's flow depth degree learning network；
High-resolution human face image is inputted Xiang teacher's flow depth degree learning network, obtains high-resolution human face depth characteristic, from
The high-resolution human face depth characteristic rich in identity information is filtered out in high-resolution human face depth characteristic, should be rich in identity information
High-resolution human face depth characteristic be to refer to correctly identify high-resolution human according to the high-resolution human face depth characteristic
The identity of face image；
The low-resolution face image of junction belt identity marks and high-resolution human face Wei Shendute rich in identity information
Sign, is finely adjusted training to student's flow depth degree learning network, obtains the trained student's flow depth that can identify low resolution face
Spend learning network；
Low-resolution face image to be identified is input to trained student's flow depth degree learning network, obtains low resolution
Rate face depth characteristic, and low resolution recognition of face is realized according to the low resolution face depth characteristic.
Further, it according to figure optimization method, filters out from high-resolution human face depth characteristic rich in identity information
The step of high-resolution human face feature, the figure optimization method includes:
According to the high-resolution human face picture construction dense graph with identity marks, the dense graph is by face vertex and connection people
Between the class inner edge or class on face vertex side form, one high-resolution human face image of a face vertex representation, a class inner edge by
A pair of generic high-resolution human face image forms, and side is by a pair of different classes of high-resolution human face image group between a class
At；
Sparse graph is constructed based on dense graph to reduce side complexity, to each generic high-resolution human in the sparse graph
Face increases a mass center vertex, and a class inner edge is made of a pair of generic high-resolution human face image, between a class side by
One mass center vertex of one high-resolution human face image and different classification composition；
Sparse graph is optimized, to filter out the high-resolution human face feature rich in identity information.
Further, when being optimized to sparse graph, by face vertex and mass center vertex representation at feature vector, base
It is expressed as Closing Binary Marker formula in the high-resolution human face depth characteristic that this feature vector will be enriched in identity information, to the Closing Binary Marker
Formula optimizes.
Further, algorithm is cut using figure to optimize sparse graph.
Further, after student's flow depth degree learning network receives low-resolution face image, high-resolution is first generated
Rate face depth characteristic generates low resolution face depth characteristic using compression.
Further, being finely adjusted trained method to student's flow depth degree learning network is: student is flowed deep learning net
Network receives high-resolution human face depth characteristic caused by the low-resolution face image with identity marks and is forced by feature recurrence
The high-resolution human face depth characteristic for being rich in identity information, then according to after by high-resolution human face depth characteristic compression
Low resolution face depth characteristic classified by identities to the low-resolution face image with identity marks.
Further, the recurrence loss function that the feature returns and the Classification Loss function of identities collectively constitute always
Loss function obtain trained student's flow depth degree learning network by solving to total loss function.
Further, when the low resolution face depth characteristic is used for face alignment, to two width for face alignment
Low-resolution face image carries out characteristic similarity calculating, if similitude is more than a threshold value, judgement is same people.
Further, when the low resolution face depth characteristic is used for face authentication, then to low resolution face depth
Feature is classified, to determine identity.
A kind of low resolution face identification device distills module, spy including the feature based on student's flow depth degree learning network
Levy compression module and feature recognition module；
Feature distillation module is used to obtain the high-resolution rich in identity information transmitted from teacher's flow depth degree learning network
Face depth characteristic；
Feature Compression module is used for Feature Dimension Reduction, obtains the low resolution face depth characteristic with distinguishing ability；
Feature recognition module is used for low resolution recognition of face, is that a feature comparative device or one have Softmax layers of son
Network.
Further, student's flow depth degree learning network connects entirely including 10 convolutional layers, 3 maximum pond layers and 3
Connect layer, convolutional layer include several 3 × 3 convolutional layer and between several 3 × 3 convolutional layer or 3 × 3 convolutional layer with
Several 1 × 1 convolutional layer for compressive features figure number of active lanes between maximum pond layer, 1 × 1 convolutional layer can slow down
The non-thread sexuality for gradually increasing, enhancing student's flow depth degree learning network of characteristic pattern number of active lanes reduces reasoning memory, maximum
For pond layer for reducing characteristic dimension, 3 full articulamentums are Image Planes, identity layer and Softmax layers.
The beneficial effects of the present invention are: for the recognition of face problem under low resolution, especially under natural environment
Low resolution recognition of face problem, recognition methods of the invention and device have the essence close to high-resolution human face identification model
It spends, then there is great advantage in speed, memory.To the face under high definition case, face identification method of the invention and
Device also has good processing capacity.
Detailed description of the invention
Fig. 1 is a kind of flow chart of low resolution face identification method of the present invention；
Fig. 2 is student's deep learning network structure of the invention
Fig. 3 A to Fig. 3 C is the figure after dense graph, sparse graph and optimization
Fig. 4 is student's flow depth degree learning network training schematic diagram of the invention
Specific embodiment
To be clearer and more comprehensible above scheme and beneficial effect of the invention, hereafter by embodiment, and attached drawing is cooperated to make
Detailed description are as follows.
The present embodiment provides a kind of low resolution face identification method and the device of realization this method, which includes spy
Sign distillation module (CNN-D), Feature Compression module (CNN-C) and feature recognition module (CNN-R), the stream of this method cognitive phase
As shown in the lower half portion Fig. 1, step includes: journey
1) low-resolution face image is received.
2) higher-dimension depth characteristic (the i.e. high-resolution human face depth spy that module extracts low resolution face is distilled by feature
Sign).
Feature distillation module is made of several layers of before student's flow depth degree learning network, and module receives low resolution face figure
Picture exports higher-dimension depth characteristic, and this feature, which is approached, correctly proposes high-resolution human face image by teacher's flow depth degree learning network
The feature taken.
Since feature distills module, the student's flow depth degree learning network for obtaining training is in processing low-resolution face image
When have the ability that teacher's flow depth degree learning network correctly handles high-resolution human face image, i.e. student's flow depth degree learning network
Practise the correct knowledge of teacher's flow depth degree learning network.And for the knowledge of teacher's flow depth degree learning network missing or mistake, it learns
Raw flow depth degree learning network also has correct processing capacity, to obtain higher recognition of face precision.
3) Feature Dimension Reduction is carried out by Feature Compression module, obtaining low-dimensional depth characteristic, (i.e. low resolution face depth is special
Sign).
Since the depth characteristic dimension that high-resolution human face identifies is higher, cause to occupy in actual deployment in biggish
Deposit and reduce the efficiency of recognition of face.For this problem, the height of Feature Compression module of the invention to feature distillation module output
It ties up depth characteristic and carries out dimensionality reduction, obtain the low-dimensional depth characteristic with discriminating power, be conducive to promote face alignment, certification and inspection
The speed of rope, to effectively support actual deployment.
Feature Compression module is made of the several full articulamentums in centre in student's flow depth degree learning network, and feature distills module
The higher-dimension depth characteristic of output carries out dimensionality reduction by Feature Compression module, obtains low-dimensional depth characteristic.
4) by feature recognition module, judge the classification of the face, realize the identification of face.
According to recognition of face be for carry out the face alignment of 1:1 (face verification) or for 1:N (N >
1) face authentication (face identification) is configured.If it is face alignment is carried out, feature recognition module is one
A feature comparative device measures the distance between two low-dimensional depth characteristics and judges that they belong to same class or inhomogeneity；
If it is face authentication is carried out, feature recognition module is the sub-network for having Softmax layers, and low-dimensional depth characteristic is classified
At certain one kind.
Student's flow depth degree learning network is to pass through the side of selection sex knowledge distillation based on teacher's flow depth degree learning network
Method training obtains, and as shown in the uppermost portion of fig. 1, is described as follows:
Teacher's flow depth degree learning networkUsually more complicated trained deep neural network in advance,
It includes more network parameter Wt, WtBy training from identity label sets LtHigh-resolution teacher's human face data collection Dt
On drawn a large amount of knowledge, DtIt is sightless generally for student's flow depth degree learning network；Receive high-resolution
Rate facial imageAs input, higher-dimension depth characteristic is obtained by reasoning, higher-dimension depth characteristic is selected according to figure optimization
It selects, judges whether each higher-dimension depth characteristic belongs to most rich information knowledge and (be rich in the high-resolution human face depth of identity information
Feature), to pass to student's flow depth degree learning network.
Student's flow depth degree learning networkIt is a simple deep neural network, receives low resolution face
ImageAs input, it includes less network parameter Ws, WsBy from student's human face data collectionStudy obtains, wherein | Ds| it is the quantity of high-resolution human face image, student's human face data
It concentrates to every panel height resolution ratio facial imageInclude N corresponding low-resolution face image, jth width low resolution face
Image is expressed asHigh-resolution human face imageWith corresponding low-resolution face imageIdentity having the same
Mark li∈ { 1,2 ..., L }, wherein L is identity marks sum.
Be made of feature distillation module, Feature Compression module and feature recognition module, returned using feature and
The study of two tasks of identities is trained, and it is deep to generate higher-dimension in the last layer (referred to as Image Planes) of feature distillation module
Feature is spent, the higher-dimension depth characteristic selected from teacher's flow depth degree learning network is approached by feature recurrence；Then by spy
It levies compression module and generates low-dimensional depth characteristic；Last low-dimensional depth characteristic enters feature recognition module, is realized by identities
Classify to the low-resolution face image of student's stream.The combination learning with two tasks of identities is returned by feature,
Make student's flow depth degree learning networkOn the one hand it can be come fromMost rich information knowledge, another party
Face can also recover the knowledge that teacher's flow network does not provide.
As shown in Fig. 2, student's flow depth degree learning network is mainly made of 3 × 3 convolutional layer, 1 × 1 convolutional layer is placed
Between 3 × 3 convolutional layer or between maximum pond layer and 3 × 3 convolutional layer, slow down the gradually increasing of characteristic pattern number of active lanes
Add, enhance the non-thread sexuality of student's flow depth degree learning network, and reduces reasoning memory；In addition, global average pond layer by with
In carrying out prediction processing, maximum pond layer is for reducing characteristic dimension；The Image Planes of 1 × 1 × D are for receiving from teacher's flow depth degree
The knowledge selected in learning network, D are characteristic dimension of teacher's flow depth degree network to high-resolution human face image zooming-out.Finally,
Student's flow depth degree learning network includes 10 convolutional layers, 3 maximum pond layers and 3 full articulamentums.To obtained network ginseng
Quantity is 0.79M, only the 0.57% of teacher's flow depth degree learning network parameter amount, therefore student's flow depth degree learning network obtains
Greatly compress.
As shown in Figure 3A to 3C, in order to select the knowledge of teacher's flow depth degree learning network, the present invention is to high-resolution human face
Image selects with passing through the feature progress distinctive after teacher's flow depth degree learning network, and benchmark is selected to flow deep learning for teacher
The face characteristic that network can correctly identify the identity of high-resolution human face image is used as most rich information knowledge.Due to for instructing
The facial image identity of the identity and training teacher's flow depth degree learning network of practicing the facial image of student's flow depth degree learning network is logical
Often inconsistent, i.e. teacher's flow depth degree learning network cannot having to explicitly know whether high-resolution human face image identifies correctly.For this purpose,
We are bright to use figure optimization method, by considering training teacher's flow depth degree learning network to the spy of high-resolution human face image zooming-out
Distinctive between sign implicitly identifies.
Specifically, it is constructed by the face characteristic dissmilarity of the similar different identity of face characteristic of same identity marks
Figure.In the figure of building, every panel height resolution ratio facial image is a vertex (referred to as face vertex), and two vertex form one
Side.If including come structure figures (referred to as dense graph) by the way of being fully connected | Ds| a vertex andSide, causes the quantity on side excessive, causes figure optimization complicated, as shown in Figure 3A.For this purpose, this hair
Bright building sparse graph is handled, as shown in Figure 3B, in sparse graph, to every group of face vertex with same identity marks
Increase a mass center, the vertex in figure is made of face vertex and mass center vertex, while by structure between the vertex of same identity marks
At internal edges and vertex be different from constituted between identity marks culminating point external edge composition, to be wrapped in sparse graph
Containing (| Ds|+L) a vertex andWherein KcIt is the face vertex number that identity marks are c, hasIt can be seen that the quantity on side is greatly reduced compared to dense graph in sparse graph, to reduce answering for figure optimization
Miscellaneous degree.Optimization Solution hereafter is carried out to sparse graph, the figure after being optimized, as shown in Figure 3 C.
In order to optimize to sparse graph, the present invention at feature vector form, divides on face vertex and mass center vertex representation
It is not as follows:
Wherein,It is the sub-network of several layers of compositions before teacher's flow depth degree learning network, for high-resolution human
Face imageExtract face characteristic；Indicator function δ (li=c) it indicates to work as liOtherwise it is 0 that value, which is 1, when=c.As it can be seen that each face
Vertex is portrayed by specific appearance characteristics, and mass center vertex is then indicated by average appearance.
Given sparse graph, the selection of most rich information knowledge can be expressed as Closing Binary Marker formula below:
Meet αi∈ { 0,1 }, i=1,2 ..., | Ds| (2)
Wherein, α is binary set, i-th of element αi=1 indicates from face characteristic fiIt is selected as most rich information knowledge；
Function d () is used to measure the similitude between two feature vectors using COS distance.As it can be seen that first item is inclined in formula (2)
To the face characteristic in selection with the average face characteristic of other identity marks with less information, λ < 0 is negative weight for putting down
Weighing two influences that Section 2 is made to tend to the face characteristic that selection has high similarity with the face of same identity marks.
Particularly, using nonnegative function d () and negative weight λ, first item is made to tend to select less face characteristic, Section 2 tendency
In selecting more face characteristics, by this two containing, so as to select most rich information knowledge.The present invention is using figure
(Graph Cut) algorithm is cut (referring to Y.Boykov and V.Kolmogorov, " An experimental comparison
of mincut/max-flow algorithms for energy minimization in computer vision,”
IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI),vol.26,
No.9, pp.1124-1137,2004) it is solved.After solution formula (2), the present invention can select limited quantity
Face characteristic, corresponding face have similitude between similitude and low class in high class.It is handled by selection, due to teacher's flow depth degree
Exception knowledge caused by learning network mistake identifies can effectively be abandoned by the mode that similar characteristics cluster, much useful to know
Knowledge is distilled from teacher's flow depth degree learning network and noisy knowledge is then suppressed, so that these knowledge can be used preferably
In the training of instruction of papil flow depth degree learning network.
As shown in figure 4, using the most rich information knowledge chosen as supervision, in conjunction with low resolution face identity marks
Information, student's flow depth degree learning network handle two problems using multi-task learning simultaneously: 1) being selected by feature regression approach
Face characteristic, 2) the face clue of missing is recovered by low resolution face.
Total loss function is formed by loss and Classification Loss two parts are returned, and is passed through and is solved following problems:
Wherein,It is the parameter of feature distillation sub-network,It is the most rich information knowledge common identity with selection
Corresponding low-resolution face image is marked, β > 0 returns loss and Classification Loss defines respectively for balancing two parts loss
Are as follows:
Formula (3) is solved by backpropagation algorithm, obtains student's flow depth degree learning network model, and the model is available
In progress face authentication and recognition of face.
It is above to implement to be merely illustrative of the technical solution of the present invention rather than be limited, the ordinary skill people of this field
Member can be with modification or equivalent replacement of the technical solution of the present invention are made, without departing from the spirit and scope of the present invention, this hair
Bright protection scope should be subject to described in claims.
Claims (10)
1. a kind of low resolution face identification method, step include:
Using high-resolution human face image one deep neural network of training with identity marks, obtains teacher and flow deep learning net
Network；
A deep neural network is trained using the high-resolution human face image and low-resolution face image marked with common identity,
Obtain student's flow depth degree learning network；
High-resolution human face image is inputted Xiang teacher's flow depth degree learning network, high-resolution human face depth characteristic is obtained, from high score
The high-resolution human face depth characteristic rich in identity information is filtered out in resolution face depth characteristic, it should be rich in the height of identity information
Resolution ratio face depth characteristic is to refer to correctly identify high-resolution human face figure according to the high-resolution human face depth characteristic
The identity of picture；
The low-resolution face image of junction belt identity marks and high-resolution human face rich in identity information tie up depth characteristic, right
Student's flow depth degree learning network is finely adjusted training, obtains to identify that the trained student of low resolution face flows deep learning
Network；Low-resolution face image to be identified is input to trained student's flow depth degree learning network, obtains low resolution
Face depth characteristic, and low resolution recognition of face is realized according to the low resolution face depth characteristic.
2. the method as described in claim 1, which is characterized in that according to figure optimization method, from high-resolution human face depth characteristic
In filter out the high-resolution human face feature rich in identity information, the step of figure optimization method includes:
According to the high-resolution human face picture construction dense graph with identity marks, the dense graph is by face vertex and connection face top
Side forms between the class inner edge or class of point, and one high-resolution human face image of a face vertex representation, a class inner edge is by a pair
Generic high-resolution human face image forms, and side is made of a pair of different classes of high-resolution human face image between a class；
Sparse graph is constructed based on dense graph to reduce side complexity, and each generic high-resolution human face is increased in the sparse graph
Add a mass center vertex, a class inner edge is made of a pair of generic high-resolution human face image, and side is by one between a class
One mass center vertex of high-resolution human face image and different classification composition；
Sparse graph is optimized, the high-resolution human face feature rich in identity information is filtered out.
3. method according to claim 2, which is characterized in that when being optimized to sparse graph, by face vertex and matter
At feature vector, the high-resolution human face depth characteristic that will be enriched in identity information based on this feature vector is expressed as heart vertex representation
Closing Binary Marker formula optimizes the Closing Binary Marker formula.
4. method as claimed in claim 3, which is characterized in that cut algorithm using figure and optimized to sparse graph.
5. the method as described in claim 1, which is characterized in that student's flow depth degree learning network receives low resolution people
After face image, high-resolution human face depth characteristic is first generated, generates low resolution face depth characteristic using compression.
6. method as claimed in claim 5, which is characterized in that be finely adjusted trained method to student's flow depth degree learning network
Be: it is deep that student's flow depth degree learning network is received high-resolution human face caused by the low-resolution face image with identity marks
It spends feature and passes through the high-resolution human face depth characteristic for being rich in identity information described in feature regression approach, then according to by the high-resolution
The compressed low resolution face depth characteristic of rate face depth characteristic is by identities to the low resolution with identity marks
Rate facial image is classified.
7. method as claimed in claim 6, which is characterized in that the feature returned return loss function and identities
Classification Loss function collectively constitutes total loss function, by solving to total loss function, obtains trained student
Flow depth degree learning network.
8. the method as described in claim 1, which is characterized in that the low resolution face depth characteristic is used for face alignment
When, characteristic similarity calculating is carried out to the two width low-resolution face images for face alignment, if similitude is more than a threshold
Value then determines to be same people；When the low resolution face depth characteristic is used for face authentication, then to low resolution face depth
Feature is classified, to determine identity.
9. a kind of low resolution face identification device distills module, feature including the feature based on student's flow depth degree learning network
Compression module and feature recognition module；
The feature distillation module is used to obtain the high-resolution rich in identity information transmitted from teacher's flow depth degree learning network
Face depth characteristic；
The Feature Compression module is used for Feature Dimension Reduction, obtains the low resolution face depth characteristic with distinguishing ability；
The feature recognition module is used for low resolution recognition of face, is a feature comparative device or one containing Softmax layers of subnet
Network.
10. device as claimed in claim 9, which is characterized in that student's flow depth degree learning network include 10 convolutional layers,
3 maximum pond layers and 3 full articulamentums, the convolutional layer include several 3 × 3 convolutional layer and positioned at several 3 × 3 convolution
Several 1 × 1 volume for compressive features figure number of active lanes between layer or between 3 × 3 convolutional layer and maximum pond layer
Lamination, for the maximum pond layer for reducing characteristic dimension, this 3 full articulamentums are Image Planes, identity layer and Softmax layers.
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