CN104504376A - Age classification method and system for face images - Google Patents
Age classification method and system for face images Download PDFInfo
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- CN104504376A CN104504376A CN201410803541.8A CN201410803541A CN104504376A CN 104504376 A CN104504376 A CN 104504376A CN 201410803541 A CN201410803541 A CN 201410803541A CN 104504376 A CN104504376 A CN 104504376A
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- 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
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- 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
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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- 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 invention discloses an age classification method for face images. The age classification method comprises the following steps: collecting sample images and acquiring images to be detected; then manually annotating an age type of all the sample images and presetting standard face graphs; comparing face feature key points of the sample images or the images to be detected with face feature key points of the standard face graphs; aligning and adjusting the key points of the sample images or the images to be detected, carrying out contour extraction on the adjusted sample images or the images to be detected to obtain face contour graphs of the sample images or the images to be detected, and finally performing age classification on the face contour graphs of the images to be detected by a classification model to obtain the age type of the images to be detected. The age type annotation is carried out by a manual and machine matching mode; the learning precision of a convolutional neural network is improved; furthermore, the sample images are processed by key point alignment and contour extraction, so that obtained training data are more uniform images; therefore the precision of a training model is improved, and the age classification is more accurate.
Description
Technical field
The present invention relates to image classification method, particularly a kind of character classification by age method of facial image and the system of employing the method.
Background technology
Images Classification is the different characteristic according to reflecting in each comfortable image information, the image processing method that different classes of target area separates, it utilizes computing machine to carry out quantitative test to image, each pixel in image or image or region are incorporated into as in several classifications a certain, to replace the vision interpretation of people.
The character classification by age of facial image is a correlation technique of face recognition technology.People starts in the process of growing to manhood gradually from birth, and along with the growth of face's bone, the continuous elongated change of shape of face is large, and appearance is also changing; And entering old process from the continuous aging of youth, skin is old and feeble gradually, and wrinkle is on the increase.Therefore, if can character classification by age be carried out according to facial image thus differentiate age bracket belonging to it exactly, by the correct discriminating of identity contributed to people.
In addition, age differentiation has a lot of application in field of human-computer interaction, and such as web browser can determine whether allowing user to check some webpage according to the age of user; Automatic vending machine can be refused to sell cigarette and alcoholic beverage to minor; Public place can provide corresponding service etc. according to the demand of all ages and classes; Particularly, in image processing process, especially when U.S. face process, we need to estimate the age to the face in image, thus carry out rational effect process for it, and therefore on estimated image, the face age is a critical problem for portrait beauty treatment.
Summary of the invention
The present invention, for solving the problem, provides the character classification by age method and system of a kind of classification facial image more accurately.
For achieving the above object, the technical solution used in the present invention is:
A character classification by age method for facial image, is characterized in that, comprise the following steps:
10. collect sample image pedestrian's work of going forward side by side and mark the age type of each sample image, and preset standard face figure;
The face characteristic key point of 20. comparison sample images and the face characteristic key point of standard faces figure, key point alignment and adjustment are carried out to sample image, again the facial contour figure that contours extract obtains sample image is carried out to the sample image after adjustment, and the facial contour figure of this sample image is normalized and inputs the training that convolutional neural networks system carries out disaggregated model;
30. obtain image to be detected, the face characteristic key point of comparison image to be detected and the face characteristic key point of standard faces figure, treat detected image and carry out key point alignment and adjustment, again the facial contour figure that contours extract obtains image to be detected is carried out to the image to be detected after adjustment, and adopt described disaggregated model to carry out character classification by age in the facial contour figure of image to be detected, obtain the age type of image to be detected.
Preferably, in the training process carrying out disaggregated model, the sample image of classification error collects and performs step 10 and step 20 successively by convolutional neural networks system, until be decided to be when exceeding expected results described step 20 the disaggregated model of training out be optimal classification model, and the facial contour figure adopting this optimal classification model to treat detected image in described step 30 carries out character classification by age, obtain the age type of image to be detected.
Preferably, described age type comprises: infant, children, teenager, youth, middle age, old age.
Preferably, the face characteristic key point of comparison sample image and the face characteristic key point of standard faces figure in described step 20, key point alignment and adjustment are carried out to sample image, or, the face characteristic key point of comparison image to be detected and the face characteristic key point of standard faces figure in described step 30, treat detected image and carry out key point alignment and adjustment, mainly pass through the face characteristic key point of preset standard face figure and correspondence thereof, and according to the sample image obtained or the human face region of image to be detected and the face characteristic key point of correspondence thereof, the face characteristic key point of affined transformation to sample image or image to be detected is utilized to align and adjust, again to adjustment after sample image or image to be detected carry out contours extract be adjusted after facial contour figure, and using the facial contour figure of the facial contour figure after this adjustment as described sample image or image to be detected.
Preferably, described face characteristic key point mainly comprises eye contour, mouth, eyebrow, face mask line, forehead.
Preferably, described contours extract mainly carries out wavelet transformation to the sample image after described adjustment or image to be detected, obtains the frequency domain figure picture of low frequency component, namely described facial contour figure.
Preferably, adopt described disaggregated model to carry out in the facial contour figure of image to be detected in described step 30 age type that character classification by age obtains image to be detected, mainly the facial contour figure of described image to be detected is put into the probability that convolutional neural networks system carries out calculating each age type of image to be detected, and the maximum age type of select probability is as the age type of image to be detected.
In addition, the present invention is the corresponding character classification by age system described method providing a kind of facial image also, it is characterized in that:
Sampling unit, for collecting the age type of sample image for artificial each sample image of mark;
Edit cell, its face characteristic key point by the image to be detected of sample image described in comparison or acquisition and the face characteristic key point of standard faces figure preset, key point alignment and adjustment are carried out to sample image or image to be detected, then the facial contour figure that contours extract obtains sample image or image to be detected is carried out to the sample image after adjustment or image to be detected;
Normalization unit, is normalized the facial contour figure of described sample image or image to be detected;
Unit, inputs the facial contour figure of the sample image after normalized or image to be detected the training that convolutional neural networks system carries out disaggregated model;
Judging unit, adopts described disaggregated model to carry out character classification by age in the facial contour figure of described image to be detected, to judge the age type of described image to be detected.
The invention has the beneficial effects as follows:
The character classification by age method and system of a kind of facial image of the present invention, it marks the age type of each sample image by collecting sample image pedestrian's work of going forward side by side, and preset standard face figure, then the face characteristic key point of comparison sample image and the face characteristic key point of standard faces figure, key point alignment and adjustment are carried out to sample image, again the facial contour figure that contours extract obtains sample image is carried out to the sample image after adjustment, and the facial contour figure of this sample image is normalized and inputs the training that convolutional neural networks system carries out disaggregated model, after obtaining image to be detected, adopt the similar face characteristic key point of method comparison image to be detected and the face characteristic key point of standard faces figure, treat detected image and carry out key point alignment and adjustment, again the facial contour figure that contours extract obtains image to be detected is carried out to the image to be detected after adjustment, finally adopt described disaggregated model to carry out in the facial contour figure of image to be detected age type that character classification by age obtains image to be detected, the mode coordinated by artificial and machine thus carries out mark age type, realize the study having supervision, improve the study precision of convolutional neural networks, and utilize key point alignment and contours extract to carry out the process of sample image, the training data obtained is made to be all more unified image, thus improve the precision of training pattern, make character classification by age more accurate.
Accompanying drawing explanation
Accompanying drawing described herein is used to provide a further understanding of the present invention, forms a part of the present invention, and schematic description and description of the present invention, for explaining the present invention, does not form inappropriate limitation of the present invention.In the accompanying drawings:
Fig. 1 is the general flow chart of the character classification by age method of a kind of facial image of the present invention;
Fig. 2 is the structural representation of the character classification by age system of a kind of facial image of the present invention.
Embodiment
In order to make technical matters to be solved by this invention, technical scheme and beneficial effect clearly, understand, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.
As shown in Figure 1, the character classification by age method of a kind of facial image of the present invention, it comprises the following steps:
10. collect sample image pedestrian's work of going forward side by side and mark the age type of each sample image, and preset standard face figure;
The face characteristic key point of 20. comparison sample images and the face characteristic key point of standard faces figure, key point alignment and adjustment are carried out to sample image, again the facial contour figure that contours extract obtains sample image is carried out to the sample image after adjustment, and the facial contour figure of this sample image is normalized and inputs the training that convolutional neural networks system carries out disaggregated model;
30. obtain image to be detected, the face characteristic key point of comparison image to be detected and the face characteristic key point of standard faces figure, treat detected image and carry out key point alignment and adjustment, again the facial contour figure that contours extract obtains image to be detected is carried out to the image to be detected after adjustment, and adopt described disaggregated model to carry out character classification by age in the facial contour figure of image to be detected, obtain the age type of image to be detected.
As preferred embodiment, in the training process carrying out disaggregated model, the sample image of classification error collects and performs step 10 and step 20 successively by convolutional neural networks system, until be decided to be when exceeding expected results described step 20 the disaggregated model of training out be optimal classification model, and the facial contour figure adopting this optimal classification model to treat detected image in described step 30 carries out character classification by age, obtain the age type of image to be detected.In described step 20, the facial contour figure of sample image be normalized and input the training that convolutional neural networks system carries out disaggregated model, mainly bringing the facial contour figure of sample image good for manual sort into convolutional neural networks system and learn; And, the sample image of classification error is collected and re-starts mark, namely, during age Type-Inconsistencies for the age type of system automatic classification and manual sort, represent artificial marking error or genealogical classification mistake, need re-start and manually mark and adjust network structure, again the sample image after mark is again carried out training study again, the process of " training-> adjusts network structure-> retraining " that so repeats is until classification is correct.
In the present embodiment, network order is input layer->K the full articulamentum of group layer->->SoftMax layer, and wherein K is more than or equal to 1; Group's layer comprises convolutional layer, active coating, down-sampling layer, normalization layer; In convolutional layer, active coating, down-sampling layer, normalization layer each layer core size and export size and can carry out regulating arbitrarily, and each layer has one to input and produces an output, and the output of every one deck is as the input of lower one deck.
Wherein, the input size of input layer is Height x Weight x Channel, and wherein Weight, Height are the wide and high of input layer image, and Channel is the Color Channel of input layer image; Because the present invention uses GPU hardware to realize, Weight=Height; The channel of input picture can only be 1 or 3.
Convolutional layer:
1) size of core must be odd number, and is not more than the wide or high of this layer of input;
2) intermediate representation is wide and high by not changing during convolutional layer, and port number is variable can be constant; Can be any positive integer in theory, because the present invention uses GPU hardware to realize, be the multiple of 16 here.
Active coating:
1) active coating does not change wide, the high or port number that convolutional layer represents;
2) activation function that active coating uses includes but not limited to following type function:
f(x)＝1/(1+e
-x)
F (x)=a*tanh (b*x), a, b are any non-zero real
f(x)＝max(0,x)
f(x)＝min(a,max(0,x))
f(x)＝log(1+e
x)
f(x)＝|x|
f(x)＝x
2
f(x)＝ax+b
3) active coating is followed after convolutional layer or full connection.
Down-sampling layer:
1) down-sampling layer does not change the port number of intermediate representation;
2) drawdown ratio of down-sampling layer to image is the size of core: namely core is that the down-sampling layer of m x n can cause intermediate representation to be reduced into last layer (1/m) x (1/n), m and n can be random natural number in theory, because the present invention uses GPU hardware to realize, m=n.Such as, 15x 15x 32, by after the down-sampling of 3x 3, becomes 5x 5x 32; 15x 15x 32, by after the down-sampling of 5x 5, becomes 3x 3x 32; But 15x 15x 32 can not carry out the down-sampling of 2x 2, because 15 can not be divided exactly by 2; Be not that input size must be the power of 2, namely 16,32,64 etc., as long as input size guarantees to be sampled by all down-sampling layers.
Normalization layer:
1) normalization layer does not change any size of intermediate representation;
2) normalization layer is not necessarily, must, add normalization layer and usually can improve precision and increase calculated amount; Whether add normalization layer, the actual precision of lifting and the speed of loss after adding be seen.
General combination is: convolution-> activates-> down-sampling-> normalization.
Following situation is special:
1) when interpolation normalization layer but increases a lot of operand to precision improvement is less, cancel normalization layer, namely adopt following combination: convolution-> activation-> down-sampling;
2) in advance, effect is substantially identical for normalization layer, namely adopts following combination: convolution-> activates-> normalization-> down-sampling.
3) down-sampling layer is cancelled: convolution-> activates; Or convolution-> activates-> normalization; Down-sampling essence is to increase robustness, has the effect of the operand reducing succeeding layer in passing simultaneously; Usually have which floor down-sampling in a network, but not all " convolution-> activates " all to follow down-sampling below.
Full articulamentum:
1) can become 1 dimension by the intermediate representation after full articulamentum, be no longer 3 dimensions;
2) the full output connected can be any;
3) once enter full connection, just convolution, down-sampling or normalization cannot be carried out;
4) can active coating be connect after full connection, or continue to connect full connection.
SoftMax layer:
After being connected on full articulamentum, effect connects complete the real-valued probability become between [0,1] produced.
The network structure that the present invention finally uses is as shown in table 1.
Table 1 convolutional neural networks structure
| The number of plies | Type | Core size | Export size | Explain |
| 1 | Input layer | 32x32x3 | ||
| 2 | Convolutional layer | 5x5 | 32x32x32 | |
| 3 | Active coating | 32x32x32 | ||
| 4 | Down-sampling layer | 2x2 | 16x16x32 | f(x)＝x 2 |
| 5 | Normalization layer | 16x16x32 | Use local normalization |
| 6 | Convolutional layer | 5x5 | 16x16x16 | |
| 7 | Active coating | 16x16x16 | ||
| 8 | Down-sampling layer | 2x2 | 8x8x16 | f(x)＝|x| |
| 9 | Normalization layer | 8x8x16 | Use local normalization | |
| 10 | Full articulamentum | 6 data | ||
| 11 | SoftMax layer | 6 data |
Described disaggregated model is adopted to carry out character classification by age in the facial contour figure of image to be detected in described step 30, mainly the facial contour figure of image to be detected after key point alignment and contours extract process is put into convolutional neural networks system and carry out calculating the probability that this image to be detected belongs to each age type, and the maximum age type of select probability is as the age type of this image to be detected.Concrete mainly carry out key point by the human face region in image to be detected and to align and contours extract obtains facial contour figure, put into the input layer of neural network, after entirely connecting, obtain the probability of each label at last SoftMax layer, namely real-valued in interval [0,1]; Be divided into according to age type in the present embodiment: infant, children, teenager, youth, middle age, old age, the age label of totally 6 types, i.e. 6 data, these 6 data and equal 1; The maximum label of select probability is as the label of the age type of this image to be detected.In step 20, the facial contour figure of this sample image is normalized and inputs the training that convolutional neural networks system carries out disaggregated model, and the determination methods of its age type is similar to the above.
Preferably, described age type comprises: infant (0 to 2 years old), children (3 to 6 years old), juvenile (7 to 14 years old), young (15 to 35 years old), middle age (36 to 60 years old), old (more than 61 years old).
The face characteristic key point of comparison sample image and the face characteristic key point of standard faces figure in described step 20, key point alignment and adjustment are carried out to sample image, or, the face characteristic key point of comparison image to be detected and the face characteristic key point of standard faces figure in described step 30, treat detected image and carry out key point alignment and adjustment, mainly pass through the face characteristic key point of preset standard face figure and correspondence thereof, and according to the sample image obtained or the human face region of image to be detected and the face characteristic key point of correspondence thereof, the face characteristic key point of affined transformation to sample image or image to be detected is utilized to align and adjust, again to adjustment after sample image or image to be detected carry out contours extract be adjusted after facial contour figure, and using the facial contour figure of the facial contour figure after this adjustment as described sample image or image to be detected.
Described affined transformation affined transformation (Affine Transform) is the one of rectangular space coordinate conversion, it is the linear transformation between a kind of two-dimensional coordinate to two-dimensional coordinate, keep " grazing " (straightness of X-Y scheme, i.e. straight line or straight line can not bend after conversion, circular arc or circular arc) and " collimation " (parallelism, namely keep the relative position relation between X-Y scheme constant, parallel lines or parallel lines, the angle of cut of intersecting straight lines is constant); The face characteristic key point of affined transformation to standard faces figure is utilized to align and adjust, mainly by a series of conversion such as movement, convergent-divergent, upset, rotation, the face characteristic key points such as sample image or image to be detected and eyes, nose, face in standard faces figure are adjusted to the position corresponding with standard faces figure.
The detection of described face characteristic key point mainly utilizes ASM (Active Shape Model) algorithm, and it is divided into training and search two steps: during training, set up the position constraint of each unique point, build the local feature of each specified point; During search, the coupling of iteration; This algorithm is that prior art does not repeat at this.Described face characteristic key point mainly comprises eye contour, mouth, eyebrow, face mask line, forehead etc.
The basic thought of face mess generation first designs the standard triangle gridding meeting basic face shape and organ distribution, by defining each vertex of a triangle sequence number, obtains the topological relation between the relative position of net point and triangle gridding dough sheet; Then with the reference mark coordinate that human face characteristic point extraction algorithm obtains, calibration distortion is carried out to standard grid, thus realize the personalization face mess generation of different human face photo.
The match point put between curve adopts Lagrange's interpolation to calculate.
The generating algorithm of net point is described below:
Eye contour: have 16 points about eye contour in 88 unique points, and we need to carry out calibration location to 20 points among standard grid.We are according to para-curve on dot generation eyes in left eye angle point, right eye angle point and top; By para-curve under left eye angle point, right eye angle point and following middle dot generation eyes.All 20 point of acquisition is got four first-class horizontal ranges of para-curve.
Mouth: in 88 unique points, mouth profile has 22 points, needs in standard grid to carry out calibration location to 34 points.Generate para-curve 9 ~ 12 and carry out matching, obtain all 34 points.
Eyebrow: in 88 unique points, eyebrow has 16 points, needs in standard grid to carry out calibration location to 20 points.Generate para-curve 1,2,3 and 4 and carry out matching, obtain all 20 points.
Face mask line: have 21 points to represent face mask line in 88 unique points.And in grid chart, have 33 points represent outline line.Outline line is divided into 4 sections, uses para-curve 13 ~ 16 matching respectively.
Forehead: by the forehead trichion of actual face and standard face, both sides cheek peak calculates affine transformation matrix.The effect that forehead part plays in human face expression action is less, and the method that therefore grid of forehead part have employed affined transformation carries out approximate generation.
Other points: as the point at the places such as forehead, cheek, mouth periphery, their coordinate calculates in proportion according to the net point reserving position.
Described contours extract mainly carries out wavelet transformation to the sample image after described adjustment or image to be detected, obtains the frequency domain figure picture of low frequency component, namely described facial contour figure.Wherein wavelet transformation is the partial transformation of space (time) and frequency, thus can information extraction from signal effectively.Wavelet transformation is a kind of new transform analysis method, the thought of its inherit and development short time discrete Fourier transform localization, overcome again window size not with shortcomings such as frequency change simultaneously, can provide one with " T/F " window of frequency shift, be the ideal tools of carrying out signal time frequency analysis and process.Its principal feature is can the feature of abundant some aspect of outstanding problem by conversion.
Adopt described disaggregated model to carry out in the facial contour figure of image to be detected in described step 30 age type that character classification by age obtains image to be detected, mainly the facial contour figure of described image to be detected is put into the probability that convolutional neural networks system carries out calculating each age type of image to be detected, and the maximum age type of select probability is as the age type of image to be detected.
As shown in Figure 2, the present invention is the corresponding character classification by age system described method providing a kind of facial image also, it is characterized in that:
Sampling unit, for collecting the age type of sample image for artificial each sample image of mark;
Edit cell, its face characteristic key point by the image to be detected of sample image described in comparison or acquisition and the face characteristic key point of standard faces figure preset, key point alignment and adjustment are carried out to sample image or image to be detected, then the facial contour figure that contours extract obtains sample image or image to be detected is carried out to the sample image after adjustment or image to be detected;
Normalization unit, is normalized the facial contour figure of described sample image or image to be detected;
Unit, inputs the facial contour figure of the sample image after normalized or image to be detected the training that convolutional neural networks system carries out disaggregated model;
Judging unit, adopts described disaggregated model to carry out character classification by age in the facial contour figure of described image to be detected, to judge the age type of described image to be detected.
It should be noted that, each embodiment in this instructions all adopts the mode of going forward one by one to describe, and what each embodiment stressed is the difference with other embodiments, between each embodiment identical similar part mutually see.For the embodiment of categorizing system, due to itself and embodiment of the method basic simlarity, so description is fairly simple, relevant part illustrates see the part of embodiment of the method.And, one of ordinary skill in the art will appreciate that all or part of step realizing above-described embodiment can have been come by hardware, the hardware that also can carry out instruction relevant by program completes, described program can be stored in a kind of computer-readable recording medium, the above-mentioned storage medium mentioned can be ROM (read-only memory), disk or CD etc.
The character classification by age method and system of a kind of facial image of the present invention, it marks the age type of each sample image by collecting sample image pedestrian's work of going forward side by side, and preset standard face figure, then the face characteristic key point of comparison sample image and the face characteristic key point of standard faces figure, key point alignment and adjustment are carried out to sample image, again the facial contour figure that contours extract obtains sample image is carried out to the sample image after adjustment, and the facial contour figure of this sample image is normalized and inputs the training that convolutional neural networks system carries out disaggregated model, after obtaining image to be detected, adopt the similar face characteristic key point of method comparison image to be detected and the face characteristic key point of standard faces figure, treat detected image and carry out key point alignment and adjustment, again the facial contour figure that contours extract obtains image to be detected is carried out to the image to be detected after adjustment, finally adopt described disaggregated model to carry out in the facial contour figure of image to be detected age type that character classification by age obtains image to be detected, the mode coordinated by artificial and machine thus carries out mark age type, realize the study having supervision, improve the study precision of convolutional neural networks, and utilize key point alignment and contours extract to carry out the process of sample image, the training data obtained is made to be all more unified image, thus improve the precision of training pattern, make character classification by age more accurate.
Above-mentioned explanation illustrate and describes the preferred embodiments of the present invention, be to be understood that the present invention is not limited to the form disclosed by this paper, should not regard the eliminating to other embodiments as, and can be used for other combinations various, amendment and environment, and can in invention contemplated scope herein, changed by the technology of above-mentioned instruction or association area or knowledge.And the change that those skilled in the art carry out and change do not depart from the spirit and scope of the present invention, then all should in the protection domain of claims of the present invention.
Claims (8)
1. a character classification by age method for facial image, is characterized in that, comprise the following steps:
10. collect sample image pedestrian's work of going forward side by side and mark the age type of each sample image, and preset standard face figure;
The face characteristic key point of 20. comparison sample images and the face characteristic key point of standard faces figure, key point alignment and adjustment are carried out to sample image, again the facial contour figure that contours extract obtains sample image is carried out to the sample image after adjustment, and the facial contour figure of this sample image is normalized and inputs the training that convolutional neural networks system carries out disaggregated model;
30. obtain image to be detected, the face characteristic key point of comparison image to be detected and the face characteristic key point of standard faces figure, treat detected image and carry out key point alignment and adjustment, again the facial contour figure that contours extract obtains image to be detected is carried out to the image to be detected after adjustment, and adopt described disaggregated model to carry out character classification by age in the facial contour figure of image to be detected, obtain the age type of image to be detected.
2. the character classification by age method of a kind of facial image according to claim 1, it is characterized in that: in the training process carrying out disaggregated model, the sample image of classification error collects and performs step 10 and step 20 successively by convolutional neural networks system, until be decided to be when exceeding expected results described step 20 the disaggregated model of training out be optimal classification model, and the facial contour figure adopting this optimal classification model to treat detected image in described step 30 carries out character classification by age, obtain the age type of image to be detected.
3. the character classification by age method of a kind of facial image according to claim 1, is characterized in that: described age type comprises: infant, children, teenager, youth, middle age, old age.
4. the character classification by age method of a kind of facial image according to claim 1, it is characterized in that: the face characteristic key point of comparison sample image and the face characteristic key point of standard faces figure in described step 20, key point alignment and adjustment are carried out to sample image, or, the face characteristic key point of comparison image to be detected and the face characteristic key point of standard faces figure in described step 30, treat detected image and carry out key point alignment and adjustment, mainly pass through the face characteristic key point of preset standard face figure and correspondence thereof, and according to the sample image obtained or the human face region of image to be detected and the face characteristic key point of correspondence thereof, the face characteristic key point of affined transformation to sample image or image to be detected is utilized to align and adjust, again to adjustment after sample image or image to be detected carry out contours extract be adjusted after facial contour figure, and using the facial contour figure of the facial contour figure after this adjustment as described sample image or image to be detected.
5. the character classification by age method of a kind of facial image according to claim 1 or 2 or 3 or 4, is characterized in that: described face characteristic key point mainly comprises eye contour, mouth, eyebrow, face mask line, forehead.
6. the character classification by age method of a kind of facial image according to claim 1 or 2 or 3 or 4, it is characterized in that: described contours extract mainly carries out wavelet transformation to the sample image after described adjustment or image to be detected, obtain the frequency domain figure picture of low frequency component, namely described facial contour figure.
7. the character classification by age method of a kind of facial image according to claim 1 or 2 or 3 or 4, it is characterized in that: adopt described disaggregated model to carry out in the facial contour figure of image to be detected in described step 30 age type that character classification by age obtains image to be detected, mainly the facial contour figure of described image to be detected is put into the probability that convolutional neural networks system carries out calculating each age type of image to be detected, and the maximum age type of select probability is as the age type of image to be detected.
8. a character classification by age system for facial image, is characterized in that:
Sampling unit, for collecting the age type of sample image for artificial each sample image of mark;
Edit cell, its face characteristic key point by the image to be detected of sample image described in comparison or acquisition and the face characteristic key point of standard faces figure preset, key point alignment and adjustment are carried out to sample image or image to be detected, then the facial contour figure that contours extract obtains sample image or image to be detected is carried out to the sample image after adjustment or image to be detected;
Normalization unit, is normalized the facial contour figure of described sample image or image to be detected;
Unit, inputs the facial contour figure of the sample image after normalized or image to be detected the training that convolutional neural networks system carries out disaggregated model;
Judging unit, adopts described disaggregated model to carry out character classification by age in the facial contour figure of described image to be detected, to judge the age type of described image to be detected.
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| CN201410803541.8A CN104504376A (en) | 2014-12-22 | 2014-12-22 | Age classification method and system for face images |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102663413A (en) * | 2012-03-09 | 2012-09-12 | 中盾信安科技(江苏)有限公司 | Multi-gesture and cross-age oriented face image authentication method |
| CN103971342A (en) * | 2014-05-21 | 2014-08-06 | 厦门美图之家科技有限公司 | Image noisy point detection method based on convolution neural network |
-
2014
- 2014-12-22 CN CN201410803541.8A patent/CN104504376A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102663413A (en) * | 2012-03-09 | 2012-09-12 | 中盾信安科技(江苏)有限公司 | Multi-gesture and cross-age oriented face image authentication method |
| CN103971342A (en) * | 2014-05-21 | 2014-08-06 | 厦门美图之家科技有限公司 | Image noisy point detection method based on convolution neural network |
Non-Patent Citations (4)
| Title |
|---|
| JUNJUN0595: ""基于仿射变换的人脸对齐的实现方法报告"", 《HTTPS://WENKU.BAIDU.COM/VIEW/1F467329BD64783E09122BE1.HTML》 * |
| WEN-BING HORNG ETC.: ""Classification of Age Groups Based on Facial Features"", 《TAMKANG JOURNAL OF SCIENCE AND ENGINEERING》 * |
| 杨之光等: ""基于形状无关纹理和Boosting学习的人口统计学分类"", 《基于形状无关纹理和BOOSTING学习的人口统计学分类》 * |
| 顾华: ""基于形状特征的人脸分类研究"", 《中国优秀博硕士学位论文全文数据库（硕士）信息科技辑》 * |
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Application publication date: 20150408 |