CN104537353A - Three-dimensional face age classifying device and method based on three-dimensional point cloud - Google Patents
Three-dimensional face age classifying device and method based on three-dimensional point cloud Download PDFInfo
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Abstract
The invention discloses a three-dimensional face age classifying device and method based on three-dimensional point cloud. The device comprises a characteristic region detection unit for positioning a characteristic region of the three-dimensional point cloud, a mapping unit for mapping the three-dimensional point cloud into depth image space, a characteristic calculating unit for calculating the depth image presentation characteristics of a mapped depth image and an age classifier calculating unit for carrying out age classifying based on the depth image presentation characteristic. The method includes the steps of detecting the characteristic region, mapping the depth image, calculating the image presentation characteristic, and carrying out classifying. According to the device and method, the mode that an image presentation characteristic pool is built in cooperation with a plurality of textural characteristics is used, the characteristics of a three-dimensional depth face image are accurately described, then accurate classifying is achieved through an age random forest classifier on the basis of an image presentation characteristic set, and the classifying accuracy is high.
Description
Technical field
The present invention relates to three-dimensional face recognition technology field, particularly relate to a kind of three-dimensional face character classification by age device and method based on three-dimensional point cloud.
Background technology
Three-dimensional face identification is relative to two-dimension human face identification, there is it on illumination robust, affect the advantages such as less by the factor such as attitude and expression, therefore, after the quality of the develop rapidly of 3-D data collection technology and three-dimensional data and precision promote greatly, their research is put in this field by a lot of scholar.
The correlated characteristic that CN20101025690 proposes three-dimensional bending invariant is used for carrying out the description of face characteristic.The method, by the local feature of the bending invariant of coding three-dimensional face surface adjacent node, extracts bending invariant related features; The correlated characteristic of described bending invariant signed and adopts spectrum recurrence to carry out dimensionality reduction, obtaining major component, and use K arest neighbors sorting technique to identify three-dimensional face.But owing to needing complicated calculated amount when extracting variable correlated characteristic, the therefore further application of the method at efficiency upper limit;
CN200910197378 proposes a kind of method of full-automatic three-dimensional Face datection and posture correction.The method is by carrying out multiple dimensioned square analysis to face three-dimension curved surface, propose face area feature and detect face curved surface cursorily, and the position that nose provincial characteristics locates nose is exactly proposed, then complete face curved surface is accurately partitioned into further, after detecting nose location of root according to the range information proposition nasion provincial characteristics of face curved surface, establish a face coordinate system, and automatically carry out the correction application of face posture accordingly.This patent object is to estimate the attitude of three-dimensional face data, belongs to the data preprocessing phase of three-dimensional face recognition system.
Three-dimensional face character classification by age is an element task in three-dimensional face field.Character classification by age not only can obtain the face characteristic in human face data effectively accurately, obtains more face semantic understanding information, as of a three-dimensional face identification rough classification step, can also promote the precision of recognition system simultaneously.The difficult point of character classification by age is to describe how accurately the age characteristic of human face data and how realizes classifying accurately on the basis of feature space.
Summary of the invention
In order to solve the problems of the technologies described above, the present invention discloses a kind of three-dimensional face character classification by age device and method based on three-dimensional point cloud, and the present invention adopts following technical scheme to solve above-mentioned technical matters:
Based on a three-dimensional face character classification by age device for three-dimensional point cloud, comprising:
For the characteristic area detecting unit that three-dimensional point cloud characteristic area positions;
Three-dimensional point cloud is carried out the map unit being mapped to depth image space;
Depth image after mapping is carried out to the feature calculation unit of depth image external performance calculating, feature comprises Gabor characteristic and LBP histogram feature;
The character classification by age device computing unit of character classification by age is carried out based on depth image external performance.
Preferably, above-mentioned a kind of based in the three-dimensional face character classification by age device of three-dimensional point cloud, described characteristic area detecting unit comprises:
Feature extraction unit, extracts the individual features of three dimensional point cloud for three-dimensional point cloud region characteristic;
Characteristic area sorter unit, to the corresponding classified calculating of also just carrying out data point that feature extraction unit is extracted, judges whether it is suitable for characteristic area, and described characteristic area sorter unit is for being support vector machine or Adaboost.
Preferably, above-mentioned a kind of based in the three-dimensional face character classification by age device of three-dimensional point cloud, described map unit comprises:
According to depth information, initial three-dimensional point cloud is mapped as the mapping block of depth image;
Wave filter is utilized to carry out the denoising module of denoising to the cavity of the depth image obtained or noise information.
Preferably, above-mentioned a kind of based in the three-dimensional face character classification by age device of three-dimensional point cloud, described character classification by age device computing unit comprises:
The age random forest classifier parameters memory module of training the age random forest sorting parameter obtained is carried out for the three-dimensional face data in memory training set;
Calculate on the basis of Gabor characteristic and the LBP histogram feature set obtained at external performance, utilize age random forest sorter to calculate, realize the character classification by age device computing module of character classification by age.
The present invention also discloses a kind of three-dimensional face character classification by age method based on three-dimensional point cloud, comprises the steps:
Characteristic area detecting step, positions for three-dimensional point cloud characteristic area, as the reference data of registration, then carries out registration to input cloud data and basic human face data;
Depth image mapping step, utilizes the D coordinates value of data, and the three dimensional point cloud of detection and location is mapped as depth image;
External performance calculation procedure, to map after depth image Gabor characteristic calculate and LBP feature calculation to obtain the external performance set of three-dimensional face Gabor characteristic and LBP histogram feature;
Classifying step, carries out character classification by age calculating to the three-dimensional face data presentation characteristic set obtained, thus realizes three-dimensional face character classification by age.
Preferably, a kind of based in the three-dimensional face character classification by age method of three-dimensional point cloud, described characteristic area is nose region above-mentioned, and the step detecting nose region is as follows:
Step 1: definite threshold, determines that the threshold value of usefulness metric density is on average born in territory, is defined as thr;
Step 2: utilize depth information to choose pending data, utilize the depth information of data, is extracted in human face data within the scope of certain depth as pending data;
Step 3: the calculating of normal vector, calculates the side vector information of the human face data selected by depth information;
Step 4: zone leveling bears the calculating of usefulness metric density, bears the definition of usefulness metric density according to zone leveling, that to obtain in pending data a connected domain on average bears usefulness metric density, selects the connected domain that wherein density value is maximum;
Step 5: determine whether to find nose region, when current region threshold value is greater than predefined thr, this region is nose region, otherwise get back to step 1 restart circulation.
Preferably, above-mentioned a kind of based in the three-dimensional face character classification by age method of three-dimensional point cloud, in described characteristic area detecting step, input three dimensional point cloud and basic human face data utilize ICP algorithm to carry out registration.
Preferably, above-mentioned a kind of based in the three-dimensional face character classification by age method of three-dimensional point cloud, in described depth image mapping step, first carry out the acquisition of depth image according to depth information, then utilize median filter to compensate denoising to the noise point in the depth image after mapping.
Preferably, above-mentioned a kind of based in the three-dimensional face character classification by age method of three-dimensional point cloud, in described depth map step, mapping block is according to (the x of spatial information, y) as the reference locus mapped, the z value of spatial information answers data value as mapping pair, builds the mapping from three-dimensional point cloud to depth image.
Preferably, above-mentioned a kind of based in the three-dimensional face character classification by age method of three-dimensional point cloud, in classifying step, character classification by age calculating carried out to the three-dimensional face data presentation characteristic use age random forest sorter obtained, thus realize three-dimensional face character classification by age.
Compared with prior art, the present invention has following technique effect:
The present invention utilizes the mode building external performance pond in conjunction with multiple textural characteristics, and describe the characteristic of three dimensional depth facial image accurately, then utilize age random forest sorter to achieve Accurate classification on the basis of external performance set, nicety of grading is high.The present invention can be used as a solution of three-dimensional face character classification by age application, also can improve system accuracy as a rough classification step one of three-dimensional face classification.
Accompanying drawing explanation
Fig. 1 is present system block diagram
Fig. 2 is FB(flow block) of the present invention
Fig. 3 is three-dimensional face nose zone location schematic diagram of the present invention
Fig. 4 is the present invention's different attitude three-dimensional face registration schematic diagram
Fig. 5 is the schematic diagram of three-dimensional face noise data
Fig. 6 is the schematic diagram that three dimensional point cloud of the present invention is mapped as depth image
Fig. 7 is external performance calculation procedure schematic diagram of the present invention
Fig. 8 is random forest sorter principle schematic
Fig. 9 is that random forest sorter carries out prediction process flow diagram
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
As shown in Figure 1, the present invention discloses a kind of three-dimensional face character classification by age device based on three-dimensional point cloud, specifically comprises:
For the characteristic area detecting unit that three-dimensional point cloud characteristic area positions;
Three-dimensional point cloud is carried out the map unit being mapped to depth image space;
Depth image after mapping is carried out to the feature calculation unit of depth image external performance calculating, feature comprises Gabor characteristic and LBP histogram feature;
The character classification by age device computing unit of character classification by age is carried out based on depth image external performance.
Wherein, above-mentioned characteristic area detecting unit comprises:
Feature extraction unit, extracts the individual features of three dimensional point cloud for three-dimensional point cloud region characteristic;
Characteristic area sorter unit, to the corresponding classified calculating of also just carrying out data point that feature extraction unit is extracted, judges whether it is suitable for characteristic area, and described characteristic area sorter unit is for being support vector machine or Adaboost.
And described depth image map unit comprises further:
According to depth information, initial three-dimensional point cloud is mapped as the mapping block of depth image;
Wave filter is utilized to carry out the denoising module of denoising to the cavity of the depth image obtained or noise information.
Character classification by age device computing unit comprises:
The age random forest classifier parameters memory module of training the age random forest sorting parameter obtained is carried out for the three-dimensional face data in memory training set;
Calculate on the basis of Gabor characteristic and the LBP histogram feature set obtained at external performance, utilize age random forest sorter to calculate, realize the character classification by age device computing module of character classification by age.
Meanwhile, the present invention also discloses a kind of three-dimensional face character classification by age method based on three-dimensional point cloud, comprises the steps:
Characteristic area detecting step, positions for three-dimensional point cloud characteristic area, as the reference data of registration, then carries out registration to input cloud data and basic human face data;
Depth image mapping step, utilizes the D coordinates value of data, and the three dimensional point cloud of detection and location is mapped as depth image;
External performance calculation procedure, to map after depth image Gabor characteristic calculate and LBP feature calculation to obtain the external performance set of three-dimensional face Gabor characteristic and LBP histogram feature;
Classifying step, carries out character classification by age calculating to the three-dimensional face data presentation characteristic set obtained, thus realizes three-dimensional face character classification by age.
Because nose region has ignore density greatly, the characteristics such as curvature is obvious, characteristic area is generally nose region.As shown in Figure 3, the step positioned nose region is as follows:
Step 1: definite threshold, determines that the threshold value of usefulness metric density is on average born in territory, is defined as thr;
Step 2: utilize depth information to choose pending data, utilize the depth information of data, is extracted in human face data within the scope of certain depth as pending data;
Step 3: the calculating of normal vector, calculates the side vector information of the human face data selected by depth information;
Step 4: zone leveling bears the calculating of usefulness metric density, bears the definition of usefulness metric density according to zone leveling, that to obtain in pending data a connected domain on average bears usefulness metric density, selects the connected domain that wherein density value is maximum;
Step 5: determine whether to find nose region, when current region threshold value is greater than predefined thr, this region is nose region, otherwise get back to step 1 restart circulation.
In depth map step, mapping block according to spatial information (x, y) as map reference locus, the z value of spatial information answers data value as mapping pair, builds the mapping from three-dimensional point cloud to depth image.As shown in Figure 4, for the three-dimensional data of different attitude, behind the reference zone obtaining registration and nose region, carry out the registration of data according to ICP algorithm; Contrast before and after registration as shown in the figure.The concrete steps of ICP algorithm are as follows:
Determine matched data set pair, from the three-dimensional nose data decimation reference data point set P reference template, recycle point-to-point between nearest distance select to input the data point set Q matched with reference data in three-dimensional face;
First the matrix of 3*3 is calculated
Wherein N is the capacity of data acquisition, then the SVD doing H matrix decomposes
H＝U∧V
T
X＝VU
T
Calculate rotation matrix R and translation matrix t
When X determinant is 1, R=X;
t＝P-R*Q
Judge that whether the error between the data set RQ+t after rigid transformation and reference data set P is enough little.When this error is less than a certain threshold value, then these two three-dimensional data set realize registration; Otherwise restart until data acquisition is to realizing registration from the first step.
Certain cavity and projection is there is, as shown in Figure 5 in data after registration.In described depth image mapping step after registration, first the acquisition of depth image is carried out according to depth information, then utilize median filter to compensate denoising for the noise point (data protruding point or empty point) in the depth image after mapping, obtain final three-dimensional face depth image as shown in Figure 6.
Because character classification by age is stronger for the detail description power requirement of textural characteristics, therefore adopt the mode in conjunction with multiple Local textural feature construction feature pond herein, the feature of employing comprises Gabor and LBP feature:
Gabor characteristic belongs to the one of texture Local rehearsal feature, and its principle is as follows:
Wherein u and v defines direction in Gabor kernel function and dimensional information, z=(x, y),
Wherein k
v=k
max/ fv, φ
u=π u/4, k
maxrepresent maximum frequency domain value, f is the amount of space of Gabor kernel function yardstick in a frequency domain.Concrete Selecting parameter is k
max=pi/2,
σ=2 π, we select the Gabor kernel function of four direction and five yardsticks, and u ∈ { { 0,1,2,3,4}, select to reflect the limitation of adopted spatial information (si) and the fine degree of set direction 0,1,2,3} and v ∈ by the number in yardstick and direction.
LBP belongs to the one of texture external performance, and because the advantages such as its computing velocity is fast, texture expressive faculty is strong are widely used, its principle is as follows:
Pixel and its neighborhood territory pixel point contrast by LBP algorithm, if get P=8, R=1, more then have the LBP value of the meaning of texture features as shown in Fig. 7 (a) (c).What wherein the first width figure represented is texture bright spot, and the second width figure represents Texture Boundaries, and the 3rd width figure represents texture dim spot or smooth grain region.According to the Statistical Distribution of texture, gained LBP value is classified as 59 classes, and using this 59 class as histogrammic base configuration statistical nature vector (LBP histogram feature).
Because character classification by age belongs to many classification problems problem, as the age can be divided into: juvenile, young, in the middle age, old, the sorter therefore based on three-dimensional face features should be multi-class sorter, and as shown in Figure 8, random forest is sorter of classifying more.The structure of random forest sorter is based on decision tree classifier.Figure (a) is decision tree classifier schematic diagram, and ground floor utilizes feature X to classify, and original input data is divided into two classes; The second layer utilizes characteristic Y to classify, and original input data is divided into four classes.The classification results of data is as shown in figure (b), and decision tree is actually a kind of method being carried out by space lineoid dividing, and when each segmentation, is all divided into two in current space.
The shortcoming of decision tree is easily to cross study to training data, affects the generalization ability of sorter.Based on decision tree classifier, utilize and build the robustness that multiple decision tree classifier increases classifier system, be random forest.Decision-making device sorter principle for building random forest can be summarized as:
1. represent the number of training sample with N, the dimension of M representation feature variable, m represents the dimension (m<M) of the characteristic variable that can use when making a decision on a node of decision tree;
2., in N number of training sample, so that the mode of repeated sampling N time can be sampled form one group of training set (bootstrap sampling), and use this sample set to train the decision tree of correspondence with it;
3. for each node, a Stochastic choice m feature, and calculate partitioning scheme best on this node based on these subcharacter set.
4. strengthen the generalization ability of sorter because random forest can build multiple decision tree, therefore all can complete growth and can not beta pruning during decision tree training.
After random forest sorter has built, just predict according to flow process as shown in Figure 9, voting results are final classification results.
The present invention utilizes the mode building external performance pond in conjunction with multiple textural characteristics, and describe the characteristic of three dimensional depth facial image accurately, then utilize age random forest sorter to achieve Accurate classification on the basis of external performance set, nicety of grading is high.The present invention can be used as a solution of three-dimensional face character classification by age application, also can improve system accuracy as a rough classification step one of three-dimensional face classification.
To those skilled in the art, obviously the invention is not restricted to the details of above-mentioned one exemplary embodiment, and when not deviating from spirit of the present invention or essential characteristic, the present invention can be realized in other specific forms.Therefore, no matter from which point, all should embodiment be regarded as exemplary, and be nonrestrictive, scope of the present invention is limited by claims instead of above-mentioned explanation, and all changes be therefore intended in the implication of the equivalency by dropping on claim and scope are included in the present invention.Any Reference numeral in claim should be considered as the claim involved by limiting.
In addition, be to be understood that, although this instructions is described according to embodiment, but not each embodiment only comprises an independently technical scheme, this narrating mode of instructions is only for clarity sake, those skilled in the art should by instructions integrally, and the technical scheme in each embodiment also through appropriately combined, can form other embodiments that it will be appreciated by those skilled in the art that.
Claims (10)
1., based on a three-dimensional face character classification by age device for three-dimensional point cloud, it is characterized in that, comprising:
For the characteristic area detecting unit that three-dimensional point cloud characteristic area positions;
Three-dimensional point cloud is carried out the map unit being mapped to depth image space;
Depth image after mapping is carried out to the feature calculation unit of depth image external performance calculating, feature comprises Gabor characteristic and LBP histogram feature;
The character classification by age device computing unit of character classification by age is carried out based on depth image external performance.
2. a kind of three-dimensional face character classification by age device based on three-dimensional point cloud according to claim 1, it is characterized in that, described characteristic area detecting unit comprises:
Feature extraction unit, extracts the individual features of three dimensional point cloud for three-dimensional point cloud region characteristic;
Characteristic area sorter unit, to the corresponding classified calculating of also just carrying out data point that feature extraction unit is extracted, judges whether it is suitable for characteristic area, and described characteristic area sorter unit is for being support vector machine or Adaboost.
3. a kind of three-dimensional face character classification by age device based on three-dimensional point cloud according to claim 1, it is characterized in that, described map unit comprises:
According to depth information, initial three-dimensional point cloud is mapped as the mapping block of depth image;
Wave filter is utilized to carry out the denoising module of denoising to the cavity of the depth image obtained or noise information.
4. a kind of three-dimensional face character classification by age device based on three-dimensional point cloud according to claim 1, it is characterized in that, described character classification by age device computing unit comprises:
The age random forest classifier parameters memory module of training the age random forest sorting parameter obtained is carried out for the three-dimensional face data in memory training set;
Calculate on the basis of Gabor characteristic and the LBP histogram feature set obtained at external performance, utilize age random forest sorter to calculate, realize the character classification by age device computing module of character classification by age.
5., based on a three-dimensional face character classification by age method for three-dimensional point cloud, it is characterized in that, comprise the steps:
Characteristic area detecting step, positions for three-dimensional point cloud characteristic area, as the reference data of registration, then carries out registration to input cloud data and basic human face data;
Depth image mapping step, utilizes the D coordinates value of data, and the three dimensional point cloud of detection and location is mapped as depth image;
External performance calculation procedure, to map after depth image Gabor characteristic calculate and LBP feature calculation to obtain the external performance set of three-dimensional face Gabor characteristic and LBP histogram feature;
Classifying step, carries out character classification by age calculating to the three-dimensional face data presentation characteristic set obtained, thus realizes three-dimensional face character classification by age.
6. a kind of face identification method based on three-dimensional point cloud according to claim 5, is characterized in that, described characteristic area is nose region, and the step detecting nose region is as follows:
Step 1: definite threshold, determines that the threshold value of usefulness metric density is on average born in territory, is defined as thr;
Step 2: utilize depth information to choose pending data, utilize the depth information of data, is extracted in human face data within the scope of certain depth as pending data;
Step 3: the calculating of normal vector, calculates the side vector information of the human face data selected by depth information;
Step 4: zone leveling bears the calculating of usefulness metric density, bears the definition of usefulness metric density according to zone leveling, that to obtain in pending data a connected domain on average bears usefulness metric density, selects the connected domain that wherein density value is maximum;
Step 5: determine whether to find nose region, when current region threshold value is greater than predefined thr, this region is nose region, otherwise get back to step 1 restart circulation.
7. a kind of three-dimensional face character classification by age method based on three-dimensional point cloud according to claim 5, is characterized in that, in described characteristic area detecting step, input three dimensional point cloud and basic human face data utilize ICP algorithm to carry out registration.
8. a kind of three-dimensional face character classification by age method based on three-dimensional point cloud according to claim 5, it is characterized in that, in described depth image mapping step, first carry out the acquisition of depth image according to depth information, then utilize median filter to compensate denoising to the noise point in the depth image after mapping.
9. a kind of three-dimensional face character classification by age method based on three-dimensional point cloud according to claim 5, it is characterized in that, in described depth map step, mapping block is according to (the x of spatial information, y) as the reference locus mapped, the z value of spatial information answers data value as mapping pair, builds the mapping from three-dimensional point cloud to depth image.
10. a kind of three-dimensional face character classification by age method based on three-dimensional point cloud according to claim 5, it is characterized in that, in classifying step, character classification by age calculating is carried out to the three-dimensional face data presentation characteristic use age random forest sorter obtained, thus realize three-dimensional face character classification by age.
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