CN106529402A - Multi-task learning convolutional neural network-based face attribute analysis method - Google Patents

Multi-task learning convolutional neural network-based face attribute analysis method Download PDF

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CN106529402A
CN106529402A CN201610856231.1A CN201610856231A CN106529402A CN 106529402 A CN106529402 A CN 106529402A CN 201610856231 A CN201610856231 A CN 201610856231A CN 106529402 A CN106529402 A CN 106529402A
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convolutional neural
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CN106529402B (en
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万军
李子青
雷震
谭资昌
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Institute of Automation of Chinese Academy of Science
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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
    • 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
    • G06V40/172—Classification, e.g. identification
    • 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
    • G06V40/178—Human faces, e.g. facial parts, sketches or expressions estimating age from face image; using age information for improving recognition

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Abstract

The present invention discloses a multi-task learning convolutional neural network (CNN)-based face attribute analysis method. According to the method, based on a convolutional neural network, a multi-task learning method is adopted to carry out age estimation, gender identification and race classification on a face image simultaneously. In a traditional processing method, when face multi-attribute analysis is carried out, a plurality of times of calculation are required, and as a result, time can be wasted, and the generalization ability of a model is decreased. According to the method of the invention, three single-task networks are trained separately; the weight of a network with the lowest convergence speed is adopted to initialize the shared part of a multi-task network, and the independent parts of the multi-task network are initialized randomly; and the multi-task network is trained, so that a multi-task convolutional neural network (CNN) model can be obtained; and the trained multi-task convolutional neural network (CNN) model is adopted to carry out age, gender and race analysis on an inputted face image simultaneously, and therefore, time can be saved, and accuracy is high.

Description

Face character analysis method based on the convolutional neural networks of multi-task learning
Technical field
The present invention relates to field of image recognition, and in particular to a kind of face of the convolutional neural networks based on multi-task learning Property analysis method.
Background technology
Traditional facial image analytical technology is often just for individual task, such as age estimation, sex identification, species Deng during for face multi-attribute analysis, needing to calculate several times, very elapsed time, be extremely difficult to actual demand.Additionally, single The facial image analytical technology of task ignores the contact between each information, it is impossible to contained in making full use of facial image Information.The facial characteristics of face are different in different sexes, between not agnate, such as between men and women, between black and white ethnic group Skin exquisiteness degree, the colour of skin, skin Lightening degree etc. have differences, and the meeting such as the bright degree of skin, color and luster, wrinkle texture There is corresponding change with age, its pace of change is also different with sex, race.As can be seen here, each face is believed It is closely connected between breath, each task is carried out into independent study and can lose many useful information to a certain extent, from And reduce the generalization ability of model.
The content of the invention
In order to solve the problems referred to above of the prior art, the present invention proposes a kind of convolutional Neural net based on multi-task learning The face character analysis method of network, the generalization ability of calculating speed and model when improve face multi-attribute analysis.
The face character analysis method of the convolutional neural networks based on multi-task learning proposed by the present invention, including single task Model analysiss, multi task model training and face character judge three parts；
Single task model analysiss：
The original sample of each age facial image is carried out face critical point detection by step A1, after pedestrian's face of going forward side by side alignment The new samples comprising facial image are generated according to pre-set dimension cutting；
Step A2, the new samples generated using step A1 are respectively trained age estimation network, sex identification network, race Three single task convolutional neural networks of sorter network, the convergence rate of each network of comparison obtain a most slow list of convergence rate The weights of task convolutional neural networks；
Multi task model is trained：
Step B1, builds multitask convolutional neural networks, and the network has the output of three tasks, corresponds to the age respectively and estimates Calculate, sex is recognized and species, three tasks all adopt softmax loss functions as object function；The multitask volume Product neutral net includes the shared part exchanged for data sharing in multi-task learning and information and for calculating above-mentioned three The independent sector of individual task output；The weight initialization multitask convolution of the single task convolutional neural networks obtained using step A2 The shared part of neutral net, forms the multitask convolutional neural networks after initialization；
Step B2, using the new samples generated in step A1, trains multitask convolutional neural networks, and obtain training is more Task convolutional neural networks model；
Face character judges：
Step C1, carries out Face datection to be input into picture, judges whether comprising facial image, as schemed to input comprising if As carrying out face critical point detection, pedestrian's face of going forward side by side alignment, then according to pre-set dimension cutting is generated includes the new of facial image Picture；
Step C2, by new picture obtained by step C1, is input to the multitask convolutional neural networks model that step B2 is obtained, enters The estimation of row age, sex identification and species.
Preferably, step A1 specifically includes herein below：
Step A11, chooses each age facial image as original sample；
Step A12, carries out face critical point detection to selected original sample, obtains two key points；
Step A13, carries out the alignment of facial image according to the position and its line of two key points to original sample, described The alignment of facial image includes the rotation to original sample, scaling, translation；
Sample after aliging in step A13 is generated the new sample comprising facial image according to pre-set dimension cutting by step A14 This.
Preferably, age estimation network, sex identification network, three lists of species network are being respectively trained in step A2 During task convolutional neural networks, the condition such as learning rate, learning strategy is completely the same.
Preferably, in step B1, the shared part of multitask convolutional neural networks is the floor portions of multitask network, is wrapped Include data input and convolutional layer and pond layer.
Preferably, in step B1, the independent sector of multitask convolutional neural networks is：Age estimation, sex identification, race The network structure of three task independences of classification, each separate network structure possess independent convolutional layer, pooling layers and full connection Layer.
Preferably, multitask convolutional neural networks total losses function is：
lmulti-task=α lage+β·lgender+γ·lrace
Wherein lmulti-taskFor the total losses of multitask network, α, β, γ are respectively the weight system of default three tasks Number, lage、lgender、lraceAge estimation loss, sex identification loss respectively in multitask convolutional neural networks, Zhong Zufen Class is lost.
Preferably, in step B2, the method for training multitask convolutional neural networks is：
Step B21, randomly selects m image, is input in the new samples generated from step A1 Initialized multitask convolutional neural networks, carry out multitask and synchronously train；
Step B22, to transmission before multitask convolutional neural networks, calculates age estimation loss l respectivelyage, sex identification damage Lose lgenderL is lost with speciesrace；
Step B23, calculates the total losses l of multitask convolutional neural networksmulti-task；
Step B24：Judge whether network training restrains, deconditioning multitask convolutional neural networks are obtained if convergence Model, otherwise execution step B25；
Step B25：Using each parameter gradients of back-propagation algorithm calculating network, network is updated using stochastic gradient descent method Parameter weights；Return to step B21.
Preferably, step C1 specifically includes herein below：
Step C11, the picture to being input into detect which, whether comprising face, abandons the pictures if not comprising face, Step C12 is entered otherwise；
Step C12, the picture to being input into carry out face critical point detection, obtain two key points；
Step C13, carries out the alignment of facial image according to the position and its line of above-mentioned key point to original image, described The alignment of facial image includes the rotation to original image, scaling, translation；
Picture after aliging in step C13 is generated the new figure comprising facial image according to pre-set dimension cutting by step C14 Piece.
Preferably, step C2 specifically includes herein below：
Step C21, the new picture that step C1 is obtained is input to the multitask convolutional neural networks for having trained, is carried out Face multi-attribute analysis；
Step C22, obtains age Probability p 1 (i), and final age estimation result is the mathematical expectation at each age,Wherein output node number is k+1, and i is output node sequence number, a (i) For the corresponding age numerical value of output node i；
Step C23, obtains sex class probability p2 (i), and final sex recognition result is the sex of maximum probability, Gender_pre=argmaxiP2 (i), wherein i are sex classification sequence number；
Step C24, obtains ethnic class probability p3 (i), race of the final species result for maximum probability, race_ Pre=argmaxiP3 (i), wherein i are ethnic classification sequence number.
Preferably, step A12 and two key points described in step C12 are two central points and upper lip central point.
The present invention is trained by single task model analysiss, multi task model, then using the multi-task learning for training Convolutional neural networks are judged to many attribute of face (age, sex, race), improve meter during face multi-attribute analysis Calculate the generalization ability of speed and model.
Description of the drawings
The step of Fig. 1 is the present embodiment A1 schematic flow sheets；
The step of Fig. 2 is the present embodiment A2 schematic flow sheets；
Multitask convolutional neural networks block schematic illustrations of the Fig. 3 for the present embodiment；
The step of Fig. 4 is the present embodiment B2 schematic flow sheets；
The step of Fig. 5 is the present embodiment C1 schematic flow sheets；
The step of Fig. 6 is the present embodiment C2 schematic flow sheets.
Specific embodiment
With reference to the accompanying drawings describing the preferred embodiment of the present invention.It will be apparent to a skilled person that this A little embodiments are used only for explaining the know-why of the present invention, it is not intended that limit the scope of the invention.
Face character analysis method of the present invention based on the convolutional neural networks of multi-task learning, first carries out single task instruction Practice, find out the most slow network of convergence；The weights of the convergence for training most slow network model are assigned to into multitask convolutional Neural again The shared part of network, then carry out multitask synchronously training；This step assigns the work of weights can make multitask training become easy, Training difficulty of the multitask network for the most slow task of convergence can largely be reduced, the convergence step base of each task is made This is consistent.
The present invention includes that single task model analysiss, multi task model training and face character judge three parts；
Single task model analysiss：
The original sample of each age facial image is carried out face critical point detection by step A1, after pedestrian's face of going forward side by side alignment The new samples comprising facial image are generated according to pre-set dimension cutting；
Step A2, the new samples generated using step A1 are respectively trained age estimation network, sex identification network, race Three single task convolutional neural networks of sorter network, the convergence rate of each network of comparison obtain a most slow list of convergence rate The weights of task convolutional neural networks；
Multi task model is trained：
Step B1, builds multitask convolutional neural networks, and the network has the output of three tasks, corresponds to the age respectively and estimates Calculate, sex is recognized and species, three tasks all adopt softmax loss functions as object function；The multitask volume Product neutral net includes the shared part exchanged for data sharing in multi-task learning and information and for calculating above-mentioned three The independent sector of individual task output；The weight initialization multitask convolution of the single task convolutional neural networks obtained using step A2 The shared part of neutral net, forms the multitask convolutional neural networks after initialization；
Step B2, using the new samples generated in step A1, trains multitask convolutional neural networks, and obtain training is more Task convolutional neural networks model；
Face character judges：
Step C1, carries out Face datection to be input into picture, judges whether comprising facial image, as schemed to input comprising if As carrying out face critical point detection, pedestrian's face of going forward side by side alignment, then according to pre-set dimension cutting is generated includes the new of facial image Picture；
Step C2, by new picture obtained by step C1, is input to the multitask convolutional neural networks model that step B2 is obtained, enters The estimation of row age, sex identification and species.
As shown in figure 1, step A1 specifically includes herein below in the present embodiment：
Step A11, chooses each age facial image as original sample；
Step A12, carries out face critical point detection to selected original sample, obtains two key points；
Step A13, carries out the alignment of facial image according to the position and its line of two key points to original sample, described The alignment of facial image includes the rotation to original sample, scaling, translation；
Sample after aliging in step A13 is generated the new sample comprising facial image according to pre-set dimension cutting by step A14 This.
As shown in Fig. 2 step A2 is respectively trained age estimation network, sex identification network, species in the present embodiment During three single task convolutional neural networks of network, the condition such as learning rate, learning strategy is completely the same.Three are drawn in the present embodiment The convergence rate of task is most slow for age estimation tasks, sex identification mission and species task convergence rate substantially phase Together, so obtaining the weights of the most slow age estimation tasks network model of convergence.
It is illustrated in figure 3 the present embodiment multitask convolutional neural networks frame diagram.
In the present embodiment, bottom portion of the shared part of multitask convolutional neural networks for multitask network in step B1 Point, including data input and convolutional layer and pond layer；Realize data sharing, information exchange in multi-task learning in shared part； Three mission bit streams are shared, and are favorably improved the generalization ability of model.As shown in figure 3, the inclusion layer of the present embodiment include input, Convolutional layer 1, pond layer 1, convolutional layer 2, pond layer 2, convolutional layer 3, convolutional layer 4.
In the present embodiment, in step B1, the independent sector of multitask convolutional neural networks is：Age is estimated, sex is recognized, The network structure of three task independences of species, each separate network structure possess independent convolutional layer, pooling layers (pond Change layer) and full articulamentum, for training more single-minded feature.As shown in figure 3, the independent sector of the present embodiment is three points , age estimation, sex identification, the calculating of species and output is respectively used to, corresponding age estimation branch includes the age Convolutional layer 5, the full articulamentum 6 of age pond layer 5, age, sex estimation branch include sex convolutional layer 5, sex pond layer 5, property Infull articulamentum 6, race's estimation branch include ethnic convolutional layer 5, the full articulamentum 6 of ethnic pond layer 5, race.
In the present embodiment, single task network structure can also regard a part for multitask network as, simply other The independent sector of business is eliminated, and the structure of such as single task age network is by multitask net neutral other and ethnic two tasks Independent sector remove.
In the present embodiment, shown in multitask convolutional neural networks total losses function such as formula (1)：
lmulti-task=α lage+β·lgender+γ·lrace (1)
Wherein lmulti-taskFor the total losses of multitask network, α, β, γ are respectively the weight system of default three tasks Number, lage、lgender、lraceAge estimation loss, sex identification loss respectively in multitask convolutional neural networks, Zhong Zufen Class is lost.
In the present embodiment, in step B2 train multitask convolutional neural networks method as shown in figure 4, including：
Step B21, randomly selects m image, is input in the new samples generated from step A1 Initialized multitask convolutional neural networks, carry out multitask and synchronously train；
Step B22, to transmission before multitask convolutional neural networks, calculates age estimation loss l respectivelyage, sex identification damage Lose lgenderL is lost with speciesrace；
Step B23, calculates the total losses l of multitask convolutional neural networksmulti-task；
Step B24：Judge whether network training restrains, deconditioning multitask convolutional neural networks are obtained if convergence Model, otherwise execution step B25；
Step B25：Using each parameter gradients of back-propagation algorithm calculating network, using stochastic gradient descent method (Stochastic Gradient Descent, SGD) updates network parameter weights；Return to step B21.
In the present embodiment, step C1 it is concrete as shown in figure 5, including：
Step C11, the picture to being input into detect which, whether comprising face, abandons the pictures if not comprising face, Step C12 is entered otherwise；
Step C12, the picture to being input into carry out face critical point detection, obtain two key points；
Step C13, carries out the alignment of facial image according to the position and its line of above-mentioned key point to original image, described The alignment of facial image includes the rotation to original image, scaling, translation；
Picture after aliging in step C13 is generated the new figure comprising facial image according to pre-set dimension cutting by step C14 Piece.
In the present embodiment, step C2 it is concrete as shown in fig. 6, including：
Step C21, the new picture that step C1 is obtained is input to the multitask convolutional neural networks for having trained, forward direction Transmission, carries out face multi-attribute analysis；
Step C22, obtains age Probability p 1 (i), and final age estimation result is the mathematical expectation at each age,Wherein output node number is k+1, and i is output node sequence number, and a (i) is defeated The corresponding age numerical value of egress i；
Step C23, obtains sex class probability p2 (i), and final sex recognition result is the sex of maximum probability, Gender_pre=argmaxiP2 (i), wherein i are sex classification sequence number；
Step C24, obtains ethnic class probability p3 (i), race of the final species result for maximum probability, race_ Pre=argmaxiP3 (i), wherein i are ethnic classification sequence number.
In the present embodiment, two key points described in step A12 and step C12 are two central points and upper lip center The resolution of the pre-set dimension described in point, step A14 or C14 is 224*224.
Those skilled in the art should be able to recognize that, with reference to the side of each example of the embodiments described herein description Method step, can with electronic hardware, computer software or the two be implemented in combination in, in order to clearly demonstrate electronic hardware and The interchangeability of software, generally describes composition and the step of each example in the above description according to function.These Function actually with electronic hardware or software mode performing, the application-specific and design constraint depending on technical scheme. Those skilled in the art can use different methods to realize to each specific application described function, but this reality Now it is not considered that beyond the scope of this invention.
So far, technical scheme is described already in connection with preferred implementation shown in the drawings, but, this area Technical staff is it is easily understood that protection scope of the present invention is expressly not limited to these specific embodiments.Without departing from this On the premise of the principle of invention, those skilled in the art can make the change or replacement of equivalent to correlation technique feature, these Technical scheme after changing or replacing it is fallen within protection scope of the present invention.

Claims (10)

1. a kind of face character analysis method of the convolutional neural networks based on multi-task learning, it is characterised in that including single Business model analysiss, multi task model training and face character judge three parts；
Single task model analysiss：
The original sample of each age facial image is carried out face critical point detection by step A1, pedestrian's face of going forward side by side alignment after according to Pre-set dimension cutting generates the new samples comprising facial image；
Step A2, the new samples generated using step A1 are respectively trained age estimation network, sex identification network, species Three single task convolutional neural networks of network, the convergence rate of each network of comparison obtain a most slow single task of convergence rate The weights of convolutional neural networks；
Multi task model is trained：
Step B1, builds multitask convolutional neural networks, and the network has three task outputs, corresponds to age estimation, property respectively Not Shi Bie and species, three tasks all adopt softmax loss functions as object function；The multitask convolutional Neural Network includes the shared part exchanged for data sharing in multi-task learning and information and for calculating above three task The independent sector of output；The weight initialization multitask convolutional Neural net of the single task convolutional neural networks obtained using step A2 The shared part of network, forms the multitask convolutional neural networks after initialization；
Step B2, using the new samples generated in step A1, trains multitask convolutional neural networks, obtains the multitask for training Convolutional neural networks model；
Face character judges：
Step C1, carries out Face datection to be input into picture, judges whether comprising facial image, as entered to input picture comprising if Pedestrian's face critical point detection, pedestrian's face of going forward side by side alignment, then according to pre-set dimension cutting generates the new picture comprising facial image；
Step C2, by new picture obtained by step C1, is input to the multitask convolutional neural networks model that step B2 is obtained, carries out year Age estimation, sex identification and species.
2. method according to claim 1, it is characterised in that step A1 specifically includes herein below：
Step A11, chooses each age facial image as original sample；
Step A12, carries out face critical point detection to selected original sample, obtains two key points；
Step A13, carries out the alignment of facial image, the face according to the position and its line of two key points to original sample The alignment of image includes the rotation to original sample, scaling, translation；
Sample after aliging in step A13 is generated the new samples comprising facial image according to pre-set dimension cutting by step A14.
3. method according to claim 1, it is characterised in that be respectively trained age estimation network, sex in step A2 When identification network, three single task convolutional neural networks of species network, the condition such as learning rate, learning strategy is completely the same.
4. method according to claim 1, it is characterised in that the shared part of multitask convolutional neural networks in step B1 For the floor portions of multitask network, including data input, convolutional layer and pond layer.
5. method according to claim 4, it is characterised in that the independent sector of multitask convolutional neural networks in step B1 For：Age estimation, sex identification, the network structure of three task independences of species, each separate network structure possess independence Convolutional layer, pooling layers and full articulamentum.
6. method according to claim 5, it is characterised in that multitask convolutional neural networks total losses function is：
lmulti-task=α lage+β·lgender+γ·lrace
Wherein lmulti-taskFor the total losses of multitask network, α, β, γ are respectively the weight coefficient of default three tasks, lage、 lgender、lraceAge estimation loss, sex identification loss respectively in multitask convolutional neural networks, species loss.
7. method according to claim 6, it is characterised in that the method for training multitask convolutional neural networks in step B2 For：
Step B21, randomly selects m image in the new samples generated from step A1, be input to The multitask convolutional neural networks of beginningization, carry out multitask and synchronously train；
Step B22, to transmission before multitask convolutional neural networks, calculates age estimation loss l respectivelyage, sex identification loss lgenderL is lost with speciesrace；
Step B23, calculates the total losses l of multitask convolutional neural networksmulti-task；
Step B24：Judge whether network training restrains, deconditioning multitask convolutional neural networks mould is obtained if convergence Type, otherwise execution step B25；
Step B25：Using each parameter gradients of back-propagation algorithm calculating network, network parameter is updated using stochastic gradient descent method Weights；Return to step B21.
8. method according to claim 1, it is characterised in that step C1 specifically includes herein below：
Step C11, the picture to being input into detect which, whether comprising face, abandons the pictures, otherwise if not comprising face Into step C12；
Step C12, the picture to being input into carry out face critical point detection, obtain two key points；
Step C13, carries out the alignment of facial image, the face according to the position and its line of above-mentioned key point to original image The alignment of image includes the rotation to original image, scaling, translation；
Picture after aliging in step C13 is generated the new picture comprising facial image according to pre-set dimension cutting by step C14.
9. method according to claim 1, it is characterised in that step C2 specifically includes herein below：
Step C21, the new picture that step C1 is obtained is input to the multitask convolutional neural networks for having trained, face is carried out Multi-attribute analysis；
Step C22, obtains age Probability p 1 (i), and final age estimation result is the mathematical expectation at each age,Wherein output node number is k+1, and i is output node sequence number, and a (i) is defeated The corresponding age numerical value of egress i；
Step C23, obtains sex class probability p2 (i), sex of the final sex recognition result for maximum probability, gender_pre =argmaxiP2 (i), wherein i are sex classification sequence number；
Step C24, obtains ethnic class probability p3 (i), race of the final species result for maximum probability, race_pre= argmaxiP3 (i), wherein i are ethnic classification sequence number.
10. the method according to claim 2 or 8, it is characterised in that two described key points are two central points and upper Lip central point.
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