CN108734146A - Facial image Age estimation method, apparatus, computer equipment and storage medium - Google Patents
Facial image Age estimation method, apparatus, computer equipment and storage medium Download PDFInfo
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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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- 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 embodiment of the invention discloses a kind of facial image Age estimation method, apparatus, computer equipment and storage mediums, include the following steps：Obtain facial image to be judged；The facial image is input in preset neural network model；Character classification by age is carried out to the facial image according to the grouped data of neural network model output.When being trained to the neural network model for carrying out face Age estimation, unified expectation processing is carried out to the photo that same person is shot in different space environments, the multiple ages for obtaining same person difference photo it is expected, then the age it is expected to be ranked up the median taken in ranking results, since training sample concentrates the different photo age desired values of same people identical, pass through this kind of photo training to convergent neural network model, it is high to the age of same people scoring output stability in different environments, it is not easily susceptible to the influence of environment.
Description
Technical field
The present embodiments relate to model algorithm field, especially a kind of facial image Age estimation method, apparatus calculates
Machine equipment and storage medium.
Background technology
Higher and higher with the accuracy of face recognition algorithms, an important role of face recognition is, by right
The age of facial image classifies, and conclusion management is carried out to numerous facial images.
In the prior art, main using the method for deep learning when human body face image being compared and application class
The method flow wanted is：According to preset work purpose, until repetition training convolutional neural networks model to the model is restrained, volume
After the completion of product neural network model training, the facial image for being intended to classify or handle is input to trained convolutional neural networks mould
In type, the weight proportion that convolutional neural networks model learns according to training is classified or is handled to the facial image, thus
, it can be seen that the method for deep learning becomes one by repetition training, by model training has certain identification and judgement
System.
The inventor of the invention has found under study for action, in the prior art by training convolutional neural networks model to figure
As when being handled, same personage the face picture under different scenes obtain the result is that inconsistent, some even phases
Difference is bigger, for example often the Age estimation of people is less than normal under bright illumination condition, under the conditions of illumination is unconspicuous, often
The Age estimation of people is bigger than normal, and the amplification of data variation is difficult to make e-learning to this consistency, therefore, mould in the prior art
The stability of type is relatively low, protected from environmental larger.
Invention content
Offer of the embodiment of the present invention is capable of providing a kind of grouped data and more stablizes, smaller face figure protected from environmental
As Age estimation method, apparatus, computer equipment and storage medium.
In order to solve the above technical problems, the technical solution that the embodiment of the invention uses is：A kind of people is provided
Face image Age estimation method, includes the following steps：
Obtain facial image to be judged；
The facial image is input in preset neural network model, wherein when the training neural network model
The age desired value of the training sample set of multiple images composition of same target source is the median in multiple Age estimation values；
Character classification by age is carried out to the facial image according to the grouped data of neural network model output.
Optionally, the neural network model is equipped with multiple age categories, and each age categories correspond to a face respectively
Age criterion value；The grouped data according to neural network model output carries out character classification by age to the facial image
The step of, specifically include following step：
Obtain multiple classification values of the neural network model output；
Confirm that the corresponding age categories of the maximum classification value of numerical value are classification results in the multiple classification value；
Calling with the classification results there is the age criterion value of mapping relations it to be made to be multiplied to obtain with maximum classification value
The classification age of the facial image.
Optionally, the feature description at the classification age is：
Wherein, piIndicate the probability of output, xiIt is expressed as the corresponding age criterion value of age categories, y presentation class ages.
Optionally, described the step of obtaining facial image to be judged, include the following steps：
Obtain target video；
The timing extraction frame picture from the target video, and judge to whether there is facial image in the frame picture；
When there are the facial images that when facial image, to confirm the frame picture image be to be judged in the frame picture.
Optionally, the grouped data according to neural network model output carries out the age point to the facial image
Further include following step after the step of class：
Obtain character classification by age result；
The facial image is input to preset picture material according to the character classification by age result and understands that model is corresponding
In input channel, wherein described image content understanding model is equipped with multiple input channel, each input channel corresponds to a kind of year
Age classification；
Obtain the content understanding data of described image content understanding model output.
Optionally, the training method of the neural network model includes：
The training sample set is obtained, the training sample set includes multiple facial images of same target；
Multiple described facial images are sequentially inputted in preset first disaggregated model, obtain multiple described faces respectively
The character classification by age value of image；
The character classification by age value of multiple facial images is ranked up using numerical value as qualifications；
Confirm that character classification by age value in an intermediate position in the ranking results is the expectation point of multiple facial images
Class value.
Optionally, described to confirm that character classification by age value in an intermediate position in the ranking results is multiple described face figures
Further include following step after the step of expectation classification value of picture：
The training sample set is input in the neural network model, swashing for the neural network model output is obtained
Encourage classification value；
Compare whether the distance between the expectation classification value and the excitation classification value are less than or equal to preset first
Threshold value；
When the distance between the expectation classification value and the excitation classification value are more than preset first threshold, follow repeatedly
Ring iterative updates the weight in the neural network model by inverse algorithms, until the expectation classification value swashs with described
Terminate when encouraging the distance between classification value less than or equal to preset first threshold.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of facial image Age estimation device, including：
Acquisition module, for obtaining facial image to be judged；
Processing module, for the facial image to be input in preset neural network model, wherein the training god
The age desired value of the training sample set of multiple images composition of same target source is multiple Age estimation values when through network model
In median；
Execution module, the grouped data for being exported according to the neural network model carry out the age to the facial image
Classification.
Optionally, the neural network model is equipped with multiple age categories, and each age categories correspond to a face respectively
Age criterion value；The facial image Age estimation device further includes：
First acquisition submodule, multiple classification values for obtaining the neural network model output；
First processing submodule, for confirming the corresponding age categories of the maximum classification value of numerical value in the multiple classification value
For classification results；
First implementation sub-module, for call with the classification results have mapping relations age criterion value make its with most
Big classification value is multiplied to obtain the classification age of the facial image.
Optionally, the feature description at the classification age is：
Wherein, piIndicate the probability of output, xiIt is expressed as the corresponding age criterion value of age categories, y presentation class ages.
Optionally, the facial image Age estimation device further includes：
Second acquisition submodule, for obtaining target video；
Second processing submodule for the timing extraction frame picture from the target video, and judges in the frame picture
With the presence or absence of facial image；
Second implementation sub-module, for when there are when facial image, confirm that the frame picture image is in the frame picture
Facial image to be judged.
Optionally, the facial image Age estimation device further includes：
Third acquisition submodule, for obtaining character classification by age result；
Third handles submodule, for the facial image to be input to preset image according to the character classification by age result
In the corresponding input channel of content understanding model, wherein described image content understanding model is equipped with multiple input channel, each
Input channel corresponds to a kind of age categories；
Third implementation sub-module, the content understanding data for obtaining the output of described image content understanding model.
Optionally, the facial image Age estimation device further includes：
4th acquisition submodule, for obtaining the training sample set, the training sample set includes the more of same target
Open facial image；
Fourth process submodule, for multiple described facial images to be sequentially inputted in preset first disaggregated model,
The character classification by age value of multiple facial images is obtained respectively；
First sorting sub-module, for being carried out to the character classification by age value of multiple facial images using numerical value as qualifications
Sequence；
4th implementation sub-module, for confirming that character classification by age value in an intermediate position in the ranking results is described more
Open the expectation classification value of facial image.
Optionally, the facial image Age estimation device further includes：
First input submodule, for the training sample set to be input in the neural network model, described in acquisition
The excitation classification value of neural network model output；
Whether first compares submodule, small for comparing the distance between the expectation classification value and the excitation classification value
In or equal to preset first threshold；
5th processing submodule, for being preset when the distance between the expectation classification value and the excitation classification value are more than
First threshold when, iterative cycles iteration updates the weight in the neural network model by inverse algorithms, until the institute
Terminate when stating the distance between desired classification value and the excitation classification value less than or equal to preset first threshold.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of computer equipment, including memory and processing
Device is stored with computer-readable instruction in the memory, when the computer-readable instruction is executed by the processor so that
The processor executes the step of facial image Age estimation method described above.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of storage Jie being stored with computer-readable instruction
Matter, when the computer-readable instruction is executed by one or more processors so that one or more processors execute above-mentioned institute
The step of stating facial image Age estimation method.
The advantageous effect of the embodiment of the present invention is：It is trained to the neural network model for carrying out face Age estimation
When, unified expectation processing is carried out to the photo that same person is shot in different space environments, obtains same person difference photograph
Multiple ages of piece it is expected, then it is expected the age to be ranked up the median taken in ranking results, be shone as same people's difference
The age desired value of piece, i.e., whether the environment no matter residing photo of same people is is identical, the age desired value of all photos
All same.Then the facial image for being marked with age desired value will be input in the neural network model of Age estimation, to god
It is trained through network model, since training sample concentrates the different photo age desired values of same people identical, passes through this one kind
Photo training is high to the age of same people scoring output stability in different environments to convergent neural network model, no
It is vulnerable to the influence of environment.
Description of the drawings
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for
For those skilled in the art, without creative efforts, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 is the basic procedure schematic diagram of facial image Age estimation method of the embodiment of the present invention；
Fig. 2 is a kind of flow diagram for embodiment that the embodiment of the present invention calculates the age；
Fig. 3 is the extracting method flow diagram of facial image of the embodiment of the present invention；
Fig. 4 is the flow diagram that the embodiment of the present invention carries out facial image further content understanding；
Fig. 5 is the acquisition methods flow diagram that the embodiment of the present invention it is expected classification value；
Fig. 6 is the training method flow diagram of neural network model of the embodiment of the present invention；
Fig. 7 is the basic structure block diagram of facial image Age estimation device of the embodiment of the present invention；
Fig. 8 is computer equipment basic structure block diagram of the embodiment of the present invention.
Specific implementation mode
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention
Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described.
In some flows of description in description and claims of this specification and above-mentioned attached drawing, contain according to
Multiple operations that particular order occurs, but it should be clearly understood that these operations can not be what appears in this article suitable according to its
Sequence is executed or is executed parallel, and the serial number such as 101,102 etc. of operation is only used for distinguishing each different operation, serial number
It itself does not represent and any executes sequence.In addition, these flows may include more or fewer operations, and these operations can
To execute or execute parallel in order.It should be noted that the descriptions such as " first " herein, " second ", are for distinguishing not
Same message, equipment, module etc., does not represent sequencing, does not also limit " first " and " second " and be different type.
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, the every other implementation that those skilled in the art are obtained without creative efforts
Example, shall fall within the protection scope of the present invention.
It includes wireless communication that those skilled in the art of the present technique, which are appreciated that " terminal " used herein above, " terminal device " both,
The equipment of number receiver, only has the equipment of the wireless signal receiver of non-emissive ability, and includes receiving and transmitting hardware
Equipment, have on bidirectional communication link, can execute two-way communication reception and emit hardware equipment.This equipment
May include：Honeycomb or other communication equipments are shown with single line display or multi-line display or without multi-line
The honeycomb of device or other communication equipments；PCS (Personal Communications Service, PCS Personal Communications System), can
With combine voice, data processing, fax and/or communication ability；PDA (Personal Digital Assistant, it is personal
Digital assistants), may include radio frequency receiver, pager, the Internet/intranet access, web browser, notepad, day
It goes through and/or GPS (Global Positioning System, global positioning system) receiver；Conventional laptop and/or palm
Type computer or other equipment, have and/or the conventional laptop including radio frequency receiver and/or palmtop computer or its
His equipment." terminal " used herein above, " terminal device " they can be portable, can transport, be mounted on the vehicles (aviation,
Sea-freight and/or land) in, or be suitable for and/or be configured in local runtime, and/or with distribution form, operate in the earth
And/or any other position operation in space." terminal " used herein above, " terminal device " can also be communication terminal, on
Network termination, music/video playback terminal, such as can be PDA, MID (Mobile Internet Device, mobile Internet
Equipment) and/or mobile phone with music/video playing function, can also be the equipment such as smart television, set-top box.
Specifically referring to Fig. 1, Fig. 1 is the basic procedure schematic diagram of the present embodiment facial image Age estimation method.
As shown in Figure 1, a kind of facial image Age estimation method, includes the following steps：
S1100, facial image to be judged is obtained；
The method for obtaining facial image includes two methods of acquisition in real time and extraction storage image/video data.Acquisition in real time
It is mainly used for the real-time application of intelligent terminal (mobile phone, tablet computer and monitoring device), such as judges the application journey of user's gender
Sequence.Extraction storage image/video data is mainly used for that the image and video data of storage is further processed, and also can
Historical photograph is applied for intelligent terminal.
The acquisition of facial image can be extracted by photo, or by the frame picture to video data, obtain
Facial image.
S1200, the facial image is input in preset neural network model, wherein the training neural network
During the age desired value of the training sample set of multiple images composition of same target source is in multiple Age estimation values when model
Between be worth；
The facial image of acquisition has been input to and has been trained in advance to convergent neural network model, in present embodiment
Neural network model can be CNN convolutional neural networks model, VGG convolutional neural networks model or insightface faces
Identification model.
Neural network model is in training in present embodiment, the training sample set of use be by several (such as 100,000
People, but not limited to this) training sample set, each training sample concentration includes multiple facial images such as 10, each trains sample
The number of the facial image of this concentration is not limited to this, in the facial image number that each training sample of present embodiment is concentrated
Amount is not necessarily identical, and the more training results of picture that training sample is concentrated are better, and the robustness of model is more preferable.
Since training sample set is made of multiple facial images, and everyone has multiple varying environment states
Facial image, before multiple facial images to same people are trained, need to the age desired value of facial image into
Row anticipation, anticipation can be carried out by the way of handmarking or using trained to the convergent age is sentenced in the prior art
Disconnected model is judged.
Specifically, age judge is carried out to the multiple pictures that same people shoots in different environments first, according to the age point
Several magnitude relationships is ranked up the face age of multiple pictures, and takes median centrally located in sequencing table,
Common age desired value as same multiple facial images of people.When the facial image of a people is odd number, sequence is obtained
Median in list is as age desired value；When the facial image of a people is even number, two in sorted lists are obtained
The average value of a median is as age desired value.
Trained to convergent neural network model by above-mentioned sample training collection, due in training same people in different rings
Age expectation under border is identical, therefore, the age test number of the same people that neural network model exports under various circumstances
With higher convergence, the dispersion of data is relatively low.
S1300, character classification by age is carried out to the facial image according to the grouped data of neural network model output.
It is defeated in response to the input neural network model when facial image is input in training to convergent neural network model
Go out the grouped data to the facial image, the age of the facial image is calculated according to the grouped data.By neural network model
Class categories are associated with a fixed age, when classification results show the character classification by age resulting class of facial image, obtain
Specific probability numbers of the classification results in the category, for example, the score of the first age category associations is 50 points, and grouped data
Show that the probability that facial image belongs to the first face value classification is 0.8, then the age is that 0.8*50 is equal to 35 years old.
The above embodiment exists to same person when being trained to the neural network model for carrying out face Age estimation
The photo shot in different space environments carries out unified expectation processing, obtains multiple age periods of same person difference photo
It hopes, then the age it is expected to be ranked up the median taken in ranking results, as the age desired value of same people's difference photo,
Whether the no matter residing environment of the photo of i.e. same people is identical, the age desired value all same of all photos.Will then by
The facial image for being marked with age desired value is input in the neural network model of Age estimation, is instructed to neural network model
Practice, since training sample concentrates the different photo age desired values of same people identical, by this kind of photo training to convergent
Neural network model, it is high to the age of same people scoring output stability in different environments, it is not easily susceptible to the influence of environment.
In some embodiments, neural network model is equipped with multiple age categories, and each age categories correspond to one respectively
A face age numerical value, i.e., each age categories are corresponding to set that there are one age numerical value.Pass through age numerical value and grouped data
In conjunction with the age for calculating input facial image.Referring specifically to Fig. 2, Fig. 2 is a kind of embodiment that the present embodiment calculates the age
Flow diagram.
As described in Figure 2, step S1300 specifically includes following step：
S1311, the multiple classification values for obtaining the neural network model output；
Since convolutional neural networks model is equipped with multiple age categories, and each age categories correspond to a face age respectively
Numerical value, therefore, the grouped data of neural network model output are the probability value that facial image belongs to each age categories.It obtains each
The corresponding probability value of age categories, and drop power sequence is carried out to each probability value according to the size of numerical value.
S1312, confirm that the corresponding age categories of the maximum classification value of numerical value are classification results in the multiple classification value；
Maximum classification value in multiple classification values is obtained according to ranking results, i.e., is arranged in primary point in ranking results
Class value, the classification value correspond to an age categories.Illustrate that the classification results of neural network model show that facial image belongs to such
Other maximum probability, i.e. classification results show that the age categories of facial image belong to the corresponding age class of the maximum number of classification value
Not.
There is the age criterion value of mapping relations to make itself and maximum classification value phase for S1313, calling and the classification results
The multiplied classification age to the facial image.
The corresponding face age numerical value of the classification results is obtained after confirming classification results.In present embodiment, by the year of people
Age is divided into different age brackets, such as [0,2], [3,8], [9,13], [14,18], [19,25], [26,30], [31,35],
[35,40],[40,50],[50,60],[60+].The division of age bracket is not limited to this, according to the difference of concrete application scene,
The division span of age bracket can bigger, for example, [0,10], [10,30] and [30,60]；Also the span of division can be made smaller,
Such as each age gap for dividing span is no more than five years old.
When setting each age categories, a basic face age numerical value, age number are defined for each age categories
Value is a value in age bracket span section, for example, the age numerical value in [0,2] age bracket is 1, in [3,8] age bracket
Age numerical value be 6, identical value mode defines the age numerical value in all age group, and age numerical value is the age bracket
Scale value.In some embodiments, the age numerical value of each age group is the maximum value for taking section, for example, in [0,2] age bracket
Age numerical value be 2, the age numerical value in [3,8] age bracket is 8, and so on.
To make that there is difference between the score in each character classification by age section, personalized difference is embodied, using face year
The result that age numerical value is multiplied with maximum classification value is as final age score.For example, the first age category associations
Age numerical value is 40 points, and grouped data shows that the probability that facial image belongs to the category is 0.9, then age score is 0.9*40
Equal to 36 years old.
Ground is further arrived, above-mentioned calculation is formulated, the feature description of character classification by age is：
Wherein, piIndicate the probability of output, xiIt is expressed as the corresponding age criterion value of age categories, value can be difference
For one in 1,6,11,15,22,27,33,37,45 and 55, xiValue it is not limited to this, in some embodiments,
xiAge numerical value for each age group is the maximum value for taking section, y presentation class ages.
In some embodiments, the neural network model in present embodiment is trained for dividing short-sighted frequency
Class.Therefore, it is necessary to be extracted to the facial image in short-sighted frequency.Specifically, referring to Fig. 3, Fig. 3 is the present embodiment face figure
The extracting method flow diagram of picture.
As shown in figure 3, further including following step before step S1100：
S1011, target video is obtained；
Target video is the video data that client uploads, and received server-side is classified to the video data, is classified
The result is that confirm video data in hero gender.
S1012, the timing extraction frame picture from the target video, and judge to whether there is face figure in the frame picture
Picture；
Target video is handled by Video processing software (such as OpenCV), target video is split as several
Frame picture.(such as extracting the mode of a pictures per 0.5s) by way of timing extraction, in several frame pictures successively
Multiple frame pictures are extracted, then frame picture is input in preset human face recognition model, whether there is people in judgment frame picture
Face image.Human face recognition model can be in the prior art it is trained for judge facial image whether there is or not CNN convolutional neural networks
Model, VGG convolutional neural networks model or insightface human face recognition models.
S1013, when there are the face figures that when facial image, to confirm the frame picture image be to be judged in the frame picture
Picture.
When judging that there are the facial images that when facial image, to confirm the frame picture image be to be judged in frame picture.And
The frame picture is input in the neural network model in present embodiment, to one or more of video hero's progressive
It does not calculate.
Due to the environment residing for the facial image of hero in different frame picture in video information be it is changeable, by using
Neural network model in present embodiment can filter the interference of environmental factor to the greatest extent, accurately determine video money
The gender score of hero in material.
In some embodiments, the character classification by age result in the present embodiment is used as and is more accurately divided facial image
The foundation of analysis.For example, in present embodiment, to facial image into after character classification by age, facial image is input to other content reason
Further classified in solution model.Referring specifically to Fig. 4, Fig. 4 is that the present embodiment carries out further content to facial image
The flow diagram of understanding.
As shown in figure 4, further including following step after step S1300：
S1321, character classification by age result is obtained；
After neural network model exports character classification by age result, terminal or server system obtain the character classification by age result.
S1322, the facial image is input to by preset picture material according to the character classification by age result understands model
In corresponding input channel, wherein described image content understanding model is equipped with multiple input channel, each input channel corresponds to
A kind of age categories；
In present embodiment, content understanding model can be CNN convolutional neural networks model, VGG convolutional neural networks moulds
Type or insightface human face recognition models.
Content understanding model is further classified to facial image, and content understanding can be carried out to facial image
(being not limited to)：A kind of in face value, gender or race classifies.
In order to make picture material understand more accurate and more specific aim, in present embodiment, picture material understands mould
The particular number that type is equipped with multiple a channel channels is related with the class interval that neural network model is set, that is, has several age brackets
Division, content understanding model just sets accordingly there are one channel.The division of channel and age bracket has one-to-one relationship.
Each channel corresponds to a kind of age type, and picture material understands that multiple channels are arranged in training in model, wherein
The feature of the facial image of the corresponding age bracket characterized to it in each channel extracts, using this setting, training to receipts
The picture material held back understands that model difference channel all has age attribute, and the facial image for capableing of pair age being adapted to it is made
More accurate content understanding classification results.In the present embodiment, feature that the channel of content understanding model is made of convolutional layer
Extract channel.
S1323, the content understanding data for obtaining the output of described image content understanding model.
After picture material understands that facial image is further processed in model, the content understanding number of its output is obtained
According to.Content understanding data are to (being not limited to)：A kind of classification results in face value, age or race.
It is above-mentioned to be in embodiment, since picture material understands that multiple channels of model are respectively provided with age attribute, nerve
After the age of facial image is identified in network model, facial image is input to by picture material according to recognition result and understands mould
Type corresponds in the channel at age, and further image procossing is carried out to facial image.Due to the age attribute in channel, to face
The age of image classifies, and picture material can be made to understand that the classification results of model are more accurate.
In present embodiment, further includes the training method of neural network model, be the present embodiment referring specifically to Fig. 5, Fig. 5
It is expected that the acquisition methods flow diagram of classification value.
As shown in figure 5, including the following steps：
S2100, the training sample set is obtained, the training sample set includes multiple facial images of same target；
The training sample set of the present embodiment is obtained by web crawlers or existing image data base.
Neural network model is in training in present embodiment, using several training sample sets (such as 100,000 people),
In, each training sample set includes the multiple facial images of a people.It is trained in multiple facial images to same people
Before, it needs to prejudge the age desired value of each training sample set, anticipation can use trained in the prior art
Judged to convergent face value judgment models.For example, CNN convolutional neural networks model, VGG convolutional neural networks model or
Insightface human face recognition models.
In present embodiment, the age expectation for multiple facial images that same training sample is concentrated is identical.
Trained to convergent neural network model by above-mentioned sample training collection, due in training same people in different rings
Age expectation under border is identical, therefore, the face value test number of the same people that neural network model exports under various circumstances
With higher convergence, the dispersion of data is relatively low.
Same target refers to same person in this reality embodiment.
S2200, multiple described facial images are sequentially inputted in preset first disaggregated model, are obtained respectively described more
Open the character classification by age value of facial image；
First disaggregated model is already existing all kinds of age Calculating models in the prior art, first in present embodiment
Disaggregated model can be CNN convolutional neural networks model, VGG convolutional neural networks model or insightface recognitions of face
Model.
Age judge is carried out to the multiple pictures that same people shoots in different environments first, specifically, by multiple faces
Image is sequentially inputted in the first disaggregated model, obtains the age score of each facial image.For example, the first age category associations
Age numerical value is 40 points, and grouped data shows that the probability that facial image belongs to the category is 0.9, then age score is 0.9*40
Equal to 36 years old.
S2300, the character classification by age value of multiple facial images is ranked up using numerical value as qualifications；
After getting part Age estimation result of the multiple pictures of same people, with the magnitude relationship of age score, to multiple
The face age of photo carries out drop power sequence.
S2400, confirm that character classification by age value in an intermediate position in the ranking results is multiple facial images
It is expected that classification value.
Median centrally located in sequencing table is taken, the common age as same multiple facial images of people it is expected
Value.When the facial image of a people is odd number, the median in sorted lists is obtained as age desired value；As a people
Facial image be even number when, obtain sorted lists in two medians average value as the age expectation.
In some embodiments, when training sample concentrates the expectation classification value of everyone multiple facial images unified
After it is expected classification value for the age in an intermediate position in ranking results, neural network model is carried out using above-mentioned facial image
Training.It is the training method flow diagram of the present embodiment neural network model referring specifically to Fig. 6, Fig. 6.
As shown in fig. 6, further including following step after step S2400：
S2500, the training sample set is input in the neural network model, it is defeated obtains the neural network model
The excitation classification value gone out；
The facial image that training sample is concentrated is sequentially inputted in neural network model, neural network model is to face figure
As carrying out feature extraction and classification.
Excitation classification value is the character classification by age data that convolutional neural networks model is exported according to the facial image of input,
Neural network model is not trained to before convergence, and excitation classification value is the larger numerical value of discreteness, when neural network model not
It is trained to convergence, excitation classification value is metastable data.
S2600, the distance between the expectation classification value and the excitation classification value are compared whether less than or equal to default
First threshold；
Judge that the excitation classification value of the full articulamentum output of neural network model and the expectation of setting are classified by loss function
Whether value is consistent, when result is inconsistent, needs to be adjusted the weight in first passage by back-propagation algorithm.
In some embodiments, loss function by calculating encourage classification value and setting it is expected between classification value away from
From (Euclidean distance or space length), whether the expectation classification value to determine excitation classification value and setting is consistent, setting first
Threshold value (for example, 0.05), when the distance between expectation classification value for encouraging classification value and setting is less than or equal to first threshold,
It then determines that excitation classification value is consistent with the expectation classification value of setting, otherwise, then encourages classification value and the expectation classification value of setting not
Unanimously.
S2700, when the distance between the expectation classification value and the excitation classification value be more than preset first threshold when,
Iterative cycles iteration updates the weight in the neural network model by inverse algorithms, until the expectation classification value with
The distance between described excitation classification value terminates when being less than or equal to preset first threshold.
When the expectation classification value of the excitation classification value of neural network model and setting is inconsistent, need to use stochastic gradient
Descent algorithm is corrected the weight in neural network model, so that the output result of convolutional neural networks model is sentenced with classification
The expected result of disconnected information is identical.By several training sample sets (by all training samples when in some embodiments, training
Photo in collection, which is upset, to be trained, with increase model lean on interference performance, enhance the stability of output.) training repeatedly
With correction, when the classification of neural network model output category data and each training sample reaches and (is not limited to) with reference to information comparison
When 99.5%, training terminates.
Trained to convergent neural network model by above-mentioned sample training collection, due in training same people in different rings
Age desired value under border is identical, therefore, the age test value of the same people that neural network model exports under various circumstances
With higher convergence, the dispersion of data is relatively low.
In some embodiments, the priority joining relation without setting between step S2500 and S2400, such as when
When the expectation classification value of facial image is by manually being demarcated.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of facial image Age estimation device.Specifically ask
It is the basic structure block diagram of this implementation facial image Age estimation device refering to Fig. 7, Fig. 7.
As shown in fig. 7, facial image Age estimation device, including：Acquisition module, processing module and execution module.Wherein,
Acquisition module is for obtaining facial image to be judged；Processing module is used to facial image being input to preset neural network mould
In type, wherein the age desired value of the training sample set of multiple images composition of same target source when training neural network model
For the median in multiple Age estimation values；The grouped data that execution module is used to be exported according to neural network model is to face figure
As carrying out character classification by age.
Facial image Age estimation device is when being trained the neural network model for carrying out face Age estimation, to same
The photo that one people shoots in different space environments carries out unified expectation processing, obtains the multiple of same person difference photo
Age it is expected, then it is expected the age to be ranked up the median taken in ranking results, the age as same people's difference photo
Desired value, i.e., whether the environment no matter residing photo of same people is is identical, the age desired value all same of all photos.It will
Then the facial image for being marked with age desired value is input in the neural network model of Age estimation, to neural network model
It is trained, since training sample concentrates the different photo age desired values of same people identical, extremely by this kind of photo training
Convergent neural network model, it is high to the age of same people scoring output stability in different environments, it is not easily susceptible to environment
Influence.
In some embodiments, neural network model is equipped with multiple age categories, and each age categories correspond to one respectively
The age criterion value of a face；Facial image Age estimation device further includes：First acquisition submodule, first processing submodule and
First implementation sub-module.Wherein, the first acquisition submodule is used to obtain multiple classification values of neural network model output；At first
For confirming, the corresponding age categories of the maximum classification value of numerical value are classification results to reason submodule in multiple classification values；First executes
Submodule, which is used to call, with classification results there is the age criterion value of mapping relations it to be made to be multiplied to obtain people with maximum classification value
The classification age of face image.
In some embodiments, the feature description at age of classifying is：
Wherein, piIndicate the probability of output, xiIt is expressed as the corresponding age criterion value of age categories, y presentation class ages.
In some embodiments, facial image Age estimation device further includes：Second acquisition submodule, second processing
Module and the second implementation sub-module.Wherein, the second acquisition submodule is for obtaining target video；Second processing submodule be used for from
Timing extraction frame picture in target video, and whether there is facial image in judgment frame picture；Second implementation sub-module is for working as
There are when facial image in frame picture, acknowledgement frame picture image is facial image to be judged.
In some embodiments, facial image Age estimation device further includes：Third acquisition submodule, third processing
Module and third implementation sub-module.Third acquisition submodule is for obtaining character classification by age result；Third handles submodule and is used for root
Facial image is input to preset picture material according to character classification by age result to understand in the corresponding input channel of model, wherein figure
Picture content understanding model is equipped with multiple input channel, each input channel corresponds to a kind of age categories；Third implementation sub-module
The content understanding data of model output are understood for obtaining picture material.
In some embodiments, facial image Age estimation device further includes：4th acquisition submodule, fourth process
Module, the first sorting sub-module and the 4th implementation sub-module.Wherein, the 4th acquisition submodule is instructed for obtaining training sample set
Practice multiple facial images that sample set includes same target；Multiple facial images for being sequentially inputted to by fourth process submodule
In preset first disaggregated model, the character classification by age value of multiple facial images is obtained respectively；First sorting sub-module, for number
Value is that qualifications are ranked up the character classification by age value of multiple facial images；4th implementation sub-module, for confirming sequence knot
Character classification by age value in an intermediate position is the expectation classification value of multiple facial images in fruit.
In some embodiments, facial image Age estimation device further includes：First input submodule, the first comparer
Module and the 5th processing submodule.Wherein, the first input submodule is used to training sample set being input in neural network model,
Obtain the excitation classification value of neural network model output；First compares submodule it is expected classification value and excitation classification value for comparing
The distance between whether be less than or equal to preset first threshold；5th processing submodule is used for when desired classification value and excitation point
When the distance between class value is more than preset first threshold, iterative cycles iteration updates neural network model by inverse algorithms
In weight, terminate when until it is expected classification value with excitation the distance between classification value less than or equal to preset first threshold.
In order to solve the above technical problems, the embodiment of the present invention also provides computer equipment.It is this referring specifically to Fig. 8, Fig. 8
Embodiment computer equipment basic structure block diagram.
As shown in figure 8, the internal structure schematic diagram of computer equipment.As shown in figure 8, the computer equipment includes passing through to be
Processor, non-volatile memory medium, memory and the network interface of bus of uniting connection.Wherein, the computer equipment is non-easy
The property lost storage medium is stored with operating system, database and computer-readable instruction, and control information sequence can be stored in database
Row when the computer-readable instruction is executed by processor, may make processor to realize a kind of facial image Age estimation method.It should
The processor of computer equipment supports the operation of entire computer equipment for providing calculating and control ability.The computer is set
It can be stored with computer-readable instruction in standby memory, when which is executed by processor, may make processing
Device executes a kind of facial image Age estimation method.The network interface of the computer equipment is used for and terminal connection communication.Ability
Field technique personnel are appreciated that structure shown in Fig. 8, only with the block diagram of the relevant part-structure of application scheme, and
The restriction for the computer equipment being applied thereon to application scheme is not constituted, and specific computer equipment may include than figure
Shown in more or fewer components, either combine certain components or arranged with different components.
Processor is for executing acquisition module 2100 in Fig. 7, processing module 2200 and execution module in present embodiment
2300 concrete function, memory are stored with the program code and Various types of data executed needed for above-mentioned module.Network interface is used for
To the data transmission between user terminal or server.Memory in present embodiment is stored with facial image critical point detection
The program code and data needed for all submodules are executed in device, server is capable of the program code and data of invoking server
Execute the function of all submodules.
Computer equipment is when being trained the neural network model for carrying out face Age estimation, to same person not
The photo shot in same space environment carries out unified expectation processing, obtains the expectation of multiple ages of same person difference photo,
Then the age it is expected to be ranked up the median taken in ranking results, as the age desired value of same people's difference photo, i.e.,
Whether the no matter residing environment of the photo of same people is identical, the age desired value all same of all photos.It will then will mark
The facial image of note has age desired value is input in the neural network model of Age estimation, is instructed to neural network model
Practice, since training sample concentrates the different photo age desired values of same people identical, by this kind of photo training to convergent
Neural network model, it is high to the age of same people scoring output stability in different environments, it is not easily susceptible to the influence of environment.
The present invention also provides a kind of storage mediums being stored with computer-readable instruction, and the computer-readable instruction is by one
When a or multiple processors execute so that the facial image age described in any of the above-described embodiment of one or more processors execution is sentenced
The step of disconnected method.
One of ordinary skill in the art will appreciate that realizing all or part of flow in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, which can be stored in a computer-readable storage and be situated between
In matter, the program is when being executed, it may include such as the flow of the embodiment of above-mentioned each method.Wherein, storage medium above-mentioned can be
The non-volatile memory mediums such as magnetic disc, CD, read-only memory (Read-Only Memory, ROM) or random storage note
Recall body (Random Access Memory, RAM) etc..
It should be understood that although each step in the flow chart of attached drawing is shown successively according to the instruction of arrow,
These steps are not that the inevitable sequence indicated according to arrow executes successively.Unless expressly stating otherwise herein, these steps
Execution there is no stringent sequences to limit, can execute in the other order.Moreover, at least one in the flow chart of attached drawing
Part steps may include that either these sub-steps of multiple stages or stage are not necessarily in synchronization to multiple sub-steps
Completion is executed, but can be executed at different times, execution sequence is also not necessarily to be carried out successively, but can be with other
Either the sub-step of other steps or at least part in stage execute step in turn or alternately.
Claims (10)
1. a kind of facial image Age estimation method, which is characterized in that include the following steps：
Obtain facial image to be judged；
The facial image is input in preset neural network model, wherein same when the training neural network model
The age desired value of the training sample set of multiple images composition of target source is the median in multiple Age estimation values；
Character classification by age is carried out to the facial image according to the grouped data of neural network model output.
2. facial image Age estimation method according to claim 1, which is characterized in that the neural network model is equipped with
Multiple age categories, and each age categories correspond to the age criterion value of a face respectively；It is described according to the neural network mould
The step of grouped data of type output carries out character classification by age to the facial image, specifically includes following step：
Obtain multiple classification values of the neural network model output；
Confirm that the corresponding age categories of the maximum classification value of numerical value are classification results in the multiple classification value；
Calling with the classification results there is the age criterion value of mapping relations to make described in it is multiplied with maximum classification value to obtain
The classification age of facial image.
3. facial image Age estimation method according to claim 2, which is characterized in that the feature at the classification age is retouched
State for：
Wherein, piIndicate the probability of output, xiIt is expressed as the corresponding age criterion value of age categories, y presentation class ages.
4. facial image Sexual discriminating method according to claim 1, which is characterized in that described to obtain face to be judged
The step of image, includes the following steps：
Obtain target video；
The timing extraction frame picture from the target video, and judge to whether there is facial image in the frame picture；
When there are the facial images that when facial image, to confirm the frame picture image be to be judged in the frame picture.
5. facial image Sexual discriminating method according to claim 1, which is characterized in that described according to the neural network
Further include following step after the step of grouped data of model output carries out character classification by age to the facial image：
Obtain character classification by age result；
The facial image is input to preset picture material according to the character classification by age result and understands the corresponding input of model
In channel, wherein described image content understanding model is equipped with multiple input channel, each input channel corresponds to a kind of age class
Not；
Obtain the content understanding data of described image content understanding model output.
6. the facial image Sexual discriminating method according to claim 1-5 any one, which is characterized in that the nerve net
The training method of network model includes：
The training sample set is obtained, the training sample set includes multiple facial images of same target；
Multiple described facial images are sequentially inputted in preset first disaggregated model, obtain multiple described facial images respectively
Character classification by age value；
The character classification by age value of multiple facial images is ranked up using numerical value as qualifications；
Confirm that character classification by age value in an intermediate position in the ranking results is the expectation classification value of multiple facial images.
7. facial image Sexual discriminating method according to claim 6, which is characterized in that described to confirm the ranking results
In after character classification by age value in an intermediate position the step of being the expectation classification value of multiple facial images, further include following
Step：
The training sample set is input in the neural network model, the excitation point of the neural network model output is obtained
Class value；
Compare whether the distance between the expectation classification value and the excitation classification value are less than or equal to preset first threshold；
When the distance between the expectation classification value and the excitation classification value are more than preset first threshold, iterative cycles change
In generation, updates the weight in the neural network model by inverse algorithms, until the expectation classification value and the excitation point
The distance between class value terminates when being less than or equal to preset first threshold.
8. a kind of facial image Age estimation device, which is characterized in that including：
Acquisition module, for obtaining facial image to be judged；
Processing module, for the facial image to be input in preset neural network model, wherein the training nerve net
The age desired value of the training sample set of multiple images composition of same target source is in multiple Age estimation values when network model
Median；
Execution module, the grouped data for being exported according to the neural network model carry out the age point to the facial image
Class.
9. a kind of computer equipment, including memory and processor, it is stored with computer-readable instruction in the memory, it is described
When computer-readable instruction is executed by the processor so that the processor is executed such as any one of claim 1 to 7 right
It is required that the step of facial image Age estimation method.
10. a kind of storage medium being stored with computer-readable instruction, the computer-readable instruction is handled by one or more
When device executes so that one or more processors execute the facial image year as described in any one of claim 1 to 7 claim
The step of age judgment method.
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Application publication date: 20181102 |