CN106971180A - A kind of micro- expression recognition method based on the sparse transfer learning of voice dictionary - Google Patents

A kind of micro- expression recognition method based on the sparse transfer learning of voice dictionary Download PDF

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CN106971180A
CN106971180A CN201710346931.0A CN201710346931A CN106971180A CN 106971180 A CN106971180 A CN 106971180A CN 201710346931 A CN201710346931 A CN 201710346931A CN 106971180 A CN106971180 A CN 106971180A
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贲晛烨
冯云聪
韩民
朱雪娜
任亿
赵子君
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Abstract

A kind of micro- expression recognition method based on the sparse transfer learning of voice dictionary, including training stage and test phase.The present invention is projected to voice and micro- expression in public space by way of projection, and in order to simplify calculating, improves efficiency, and sparse dictionary expression is carried out to the data after projection；In order to further reduce the gap data in two domains, it is considered to the reconstruct between the dictionary in two domains is carried out, it is achieved thereby that the relevance of dictionary, so that the rarefaction representation matrix after projection generates bigger correlation.

Description

A kind of micro- expression recognition method based on the sparse transfer learning of voice dictionary
Technical field
The present invention relates to a kind of micro- expression recognition method based on the sparse transfer learning of voice dictionary, belong to modal idenlification with And the technical field of machine learning.
Background technology
Micro- expression be people under holddown or when attempting to hide real feelings a kind of time for revealing it is extremely short, not by Autonomous facial expression.1966, Haggard and Issacs were found that this trickle expression first.Ekman et al. is to micro- table Feelings have carried out a series of research, and micro- expression is identified a kind of clue of reliable detecting lie.In the last few years, with micro- table The proposition of feelings concept and develop rapidly, it is in safety monitoring, case investigation, network security, military affairs, the business even field such as amusement All show good application prospect.2002, micro- expression research achieved huge progress, and Ekman et al. develops micro- expression Training tool (Micro Expression Training Tool, METT), the instrument effectively raises micro- Expression Recognition energy Power.
With developing rapidly for machine learning and Expression Recognition algorithm, micro- expression automatic identification research, which is achieved, considerable enters Step.Zhang et al. proposes a kind of new differentiation feature descriptor, is extracted light stream histogram and LBP-TOP features；He et al. Propose a kind of multitask feature learning method of different weight reply different characteristic layer features；Ben et al. is proposed based on most Bigization minimizes the maximal margin projection and tensor representation of internal Laplace operator；Wang et al. passes through one tensor of searching The correlation maximization that subspace allows between sample, it is proposed that sparse tensor canonical correlation analysis；These are directed to different problems The new theory of proposition all achieves obvious raising in specific field.
Occur in spite of increasing micro- expression recognition method, but due to the limitation of training samples number, these algorithms It is difficult to train an effective model.And transfer learning has prominent advantage in this respect, transfer learning utilizes original neck The problem of knowledge in domain solves the domain of dependence.Transfer learning can be classified as three parts：Inductive learning, shift learning is unsupervised to move Move study.Chang et al. proposes using semi-supervised information to calculate the correlation between different characteristic；Yeh et al. proposes one Plant and utilize CCA by the domain-adaptive algorithm of all data projections a to public space；These methods proposed respectively have feature, For particular problem effect substantially, but at present from voice to the transfer learning of micro- expression not yet it has been proposed that.
Language and expression are two kinds of most intuitive ways that people reveal emotion.When emotion is fluctuated, the language of people can be sent out Raw obvious change, the lifting of such as tone, the speed of word speed.Therefore for the identification of micro- expression, when sample size is not enough to During support training valid model, voice is a kind of ideal aid, so the present invention relates to from abundant voice feelings Effective information is searched out in sense sample helps micro- expression to be classified.The results show, this is a kind of the effective of science Means.
The content of the invention
For at present from voice to the technical problem of the transfer learning blank of micro- expression, the present invention proposes a kind of based on language Micro- expression recognition method of the sparse transfer learning of sound dictionary.The present invention is compared with other recognition methods, and first Application is arrived in voice The identification of micro- expression, and recognition performance effectively improves.
Technical scheme is as follows：
A kind of micro- expression recognition method based on the sparse transfer learning of voice dictionary, including training stage and test phase；
The training stage comprises the following steps：
First, most representational feature is extracted to voice domain and micro- expression domain；The feature in the voice domain includes：In short-term Energy, fundamental frequency, second order MFCC coefficients, first to fourth formant, the maximum of features described above, minimum value, average, middle position Value and five groups of statistics of variance；The feature in micro- expression domain directly extracts LBP-TOP, and this feature is dropped using PCA algorithms Dimension；
Then, the characteristic extracted is grouped, the feature set in voice domain and micro- expression domain is divided into training set And test set；
Then, because the data of different field have larger difference, if directly carrying out the pairing of data, not only exist It is difficult to say logical in physical significance, actual effect is also undesirable, therefore the present invention finds an optimal throwing by iterative algorithm Shadow matrix so that the data projection in voice domain and micro- expression domain obtains voice simultaneously to a public space in public space The sparse dictionary of the sparse dictionary in domain and micro- expression, for the degree of association of the dictionary that improves two domains, to the dilute of the voice domain The sparse dictionary for dredging dictionary and micro- expression domain carries out mutual reconstruct；
Afterwards, the iteration by certain number of times and optimization, respectively obtain the dictionary in voice domain and the dictionary in micro- expression domain, language The projection matrix of range, the projection matrix in micro- expression domain, the restructuring matrix in voice domain, the restructuring matrix in micro- expression domain, voice domain Sparse coefficient representing matrix, the sparse coefficient representing matrix in micro- expression domain；
The test phase comprises the following steps：
Test set for giving voice domain and micro- expression domain, the projection matrix obtained first by on-line training is to two domains Feature set row is projected；
Then, the dictionary and the dictionary in micro- expression domain in the voice domain obtained using training, two to projecting to public space Individual characteristic of field carries out sparse reconstruct, obtains the respective sparse coefficient representing matrix in two domains；
Finally, the sparse coefficient representing matrix in two domains is carried out by machine recognition classic algorithm k nearest neighbor grader KNN Classification and Identification.
According to currently preferred, the k nearest neighbor grader KNN refers to the nearest neighbor classifier based on Euclidean distance, its It is as follows that the method for classification includes step：
First, feature extraction is carried out to voice domain and micro- expression domain, obtained Two characteristic sets, whereinThe feature of voice domain and a sample in micro- expression domain is represented respectively；mx,myRepresent respectively Voice domain and the intrinsic dimensionality in micro- expression domain, nx,nyThe sample size in voice domain and micro- expression domain is represented respectively；
Then, a pair of projection matrix W are found in trainingX,WY, by the Projection Character in two domains to public space, and pass through word Allusion quotation represents sparse, i.e.,：
Wherein,What is represented is the projection matrix in voice domain,What is represented is the projection square in micro- expression domain Battle array,What is represented is the dictionary in voice domain,Represent the dictionary in micro- expression domain； The sparse coefficient representing matrix in voice domain and micro- expression domain is represented respectively；D represents the projection dimension that two domains project to public space Degree；px,pyThe dictionary size in voice domain and micro- expression domain is represented respectively；Represent respectively voice domain and The unit matrix in micro- expression domain；
Then, in order to allow the feature that two domains project to public subspace to have similar distribution, the present invention is to each The dictionary in domain has carried out linear expression with the dictionary in another domain, and the data difference between not same area is reduced by reconstruct, represents Form is as follows：
Wherein,The dictionary restructuring matrix of reconstructed voice collection and micro- expression collection is represented respectively, | | dxi ||2≤1,||dyj||2≤1,||VX||1≤τ,||VY||1≤ τ, τ=0.001, dxi, dyjD is represented respectivelyX, DYIn column vector；
Finally, the object function of micro- expression recognition method based on the sparse transfer learning of voice dictionary is as follows：
Wherein,||dxi||2≤1,||dyj||2≤1,||Vx||1≤τ,||Vy||1≤ τ, | | Sx ||1≤σ,||Sy||1≤ σ, τ=0.001, σ=0.001；By the solution of object function, final W is obtainedX,WY,DX DY。
For above-mentioned object function, using the strategy of variable alternative optimization, successive ignition is optimal effect.
The beneficial effects of the invention are as follows：
The invention provides micro- expression recognition method based on the sparse transfer learning of voice dictionary, first Application voice is to micro- The transfer learning of expression, it is contemplated that the dictionary of same area does not have larger otherness, the present invention carries out that to the dictionary in two domains This reconstruct is represented, further enhances the association in two domains.It is compared to other several methods, convergence of algorithm speed, Time cost is low, and discrimination is significantly improved.
Brief description of the drawings
Influence of Fig. 1 difference dictionary sizes to classification；
Fig. 2 flow charts of the present invention；
Fig. 3-1, Fig. 3-2, Fig. 3-3 are respectively the speech waveform figure of three kinds of different emotions of CASIA corpus；
Different emotions sample instantiation figure in the micro- expression storehouses of Fig. 4 CASME.
Embodiment
The present invention is described in detail with example below in conjunction with the accompanying drawings, but not limited to this.
As Figure 1-4.
Embodiment 1,
A kind of micro- expression recognition method based on the sparse transfer learning of voice dictionary, including training stage and test phase；
The training stage comprises the following steps：
First, most representational feature is extracted to voice domain and micro- expression domain；The feature in the voice domain includes：In short-term Energy, fundamental frequency, second order MFCC coefficients, first to fourth formant, the maximum of features described above, minimum value, average, middle position Value and five groups of statistics of variance；The feature in micro- expression domain directly extracts LBP-TOP, and this feature is dropped using PCA algorithms Dimension；
Then, the characteristic extracted is grouped, the feature set in voice domain and micro- expression domain is divided into training set And test set；
Then, because the data of different field have larger difference, if directly carrying out the pairing of data, not only exist It is difficult to say logical in physical significance, actual effect is also undesirable, therefore the present invention finds an optimal throwing by iterative algorithm Shadow matrix so that the data projection in voice domain and micro- expression domain obtains voice simultaneously to a public space in public space The sparse dictionary of the sparse dictionary in domain and micro- expression, for the degree of association of the dictionary that improves two domains, to the dilute of the voice domain The sparse dictionary for dredging dictionary and micro- expression domain carries out mutual reconstruct；
Afterwards, the iteration by certain number of times and optimization, respectively obtain the dictionary in voice domain and the dictionary in micro- expression domain, language The projection matrix of range, the projection matrix in micro- expression domain, the restructuring matrix in voice domain, the restructuring matrix in micro- expression domain, voice domain Sparse coefficient representing matrix, the sparse coefficient representing matrix in micro- expression domain；
The test phase comprises the following steps：
Test set for giving voice domain and micro- expression domain, the projection matrix obtained first by on-line training is to two domains Feature set row is projected；
Then, the dictionary and the dictionary in micro- expression domain in the voice domain obtained using training, two to projecting to public space Individual characteristic of field carries out sparse reconstruct, obtains the respective sparse coefficient representing matrix in two domains；
Finally, the sparse coefficient representing matrix in two domains is carried out by machine recognition classic algorithm k nearest neighbor grader KNN Classification and Identification.
Embodiment 2,
Recognition methods as described in Example 1, its difference is that the k nearest neighbor grader KNN refers to be based on Euclidean distance Nearest neighbor classifier, its classify method include step it is as follows：
First, feature extraction is carried out to voice domain and micro- expression domain, obtained Two characteristic sets, whereinThe feature of voice domain and a sample in micro- expression domain is represented respectively；mx,myRepresent respectively Voice domain and the intrinsic dimensionality in micro- expression domain, nx,nyThe sample size in voice domain and micro- expression domain is represented respectively；
Then, a pair of projection matrix W are found in trainingX,WY, by the Projection Character in two domains to public space, and pass through word Allusion quotation represents sparse, i.e.,：
Wherein,What is represented is the projection matrix in voice domain,What is represented is the projection square in micro- expression domain Battle array,What is represented is the dictionary in voice domain,Represent the dictionary in micro- expression domain； The sparse coefficient representing matrix in voice domain and micro- expression domain is represented respectively；D represents the projection dimension that two domains project to public space Degree；px,pyThe dictionary size in voice domain and micro- expression domain is represented respectively；Represent respectively voice domain and The unit matrix in micro- expression domain；
Then, in order to allow the feature that two domains project to public subspace to have similar distribution, the present invention is to each The dictionary in domain has carried out linear expression with the dictionary in another domain, and the data difference between not same area is reduced by reconstruct, represents Form is as follows：
Wherein,The dictionary restructuring matrix of reconstructed voice collection and micro- expression collection is represented respectively, | | dxi ||2≤1,||dyj||2≤1,||VX||1≤τ,||VY||1≤ τ, τ=0.001, dxi, dyjD is represented respectivelyX, DYIn column vector；
Finally, the object function of micro- expression recognition method based on the sparse transfer learning of voice dictionary is as follows：
Wherein,||dxi||2≤1,||dyj||2≤1,||Vx||1≤τ,||Vy||1≤ τ, | | Sx ||1≤σ,||Sy||1≤ σ, τ=0.001, σ=0.001；By the solution of object function, final W is obtainedX,WY,DX DY。
For follow-up algorithm steps writing simply, derivative of the object function to parameters is first obtained here；
Similarly,
Algorithm complete procedure is given below：
1. initiation parameter：
DX=rand (mx,px)；DY=rand (my,py)；
SX=rand (px,nx)；SY=rand (py,ny)；
VX=rand (nx,nx)；VY=rand (ny,ny)；
Error=10；Iter=1
2.while error≥0.05||iter≤25
3. fix DX,SX,DY,SY,VX,VY, by object function respectively to WX、WYDerivation is obtained:
Due to existingThe problem of constraint, it is impossible to simply make derivative be zero solution, the present invention is adopted With generalized gradient Algorithm for Solving WX,WYValue, that is, obtain projection matrix WX、WY；
4. fix SX,SY,WX,WY,DY,VX,VY, object function is on DXConvex function, to DXDerivation is obtained:
OrderSolve:
Similarly fix SX,SY,WX,WY,DX,VX,VY, solve:
Obtain dictionary DX,DY；
5. obtain sparse coefficient representing matrix S using the OMP functions in K-SVD tool boxesX,SY, generalized time cost and reality Accuracy rate is tested, iterations is set as 15 times, S is selected from optimal resultX,SY。
6. try to achieve V using lasso algorithmsX,VY；
Iter= iter+1；
8.end
9. above-mentioned iteration obtains final SY,SX,WX,WY,DY,DX,VX,VY, utilize the projection matrix W tried to achieveX,WYTo surveying Examination data are projected:
XTe,YTeThe test data in two domains is represented respectively；ZX,ZYThe data that two domains project to public space are represented respectively Collection；Then according to dictionary DX, DYTry to achieve respective sparse coefficient representing matrix.
10. sparse coefficient representing matrix is classified using KNN.
Contrast experiment：
It is Chinese section using a kind of micro- expression recognition method based on the sparse transfer learning of voice dictionary of the present invention CASIA corpus and the micro- expression storehouses of CASME that institute's automation is recorded, this experiment are selected in above-mentioned two storehouse respectively The class sample of happiness, sadness, surprise tri-, respectively there is 60.
In order to verify influence of the different factors to classifying quality, Fig. 1 gives influence of the different dictionary sizes to classification.Figure 1 can see, and in the case where dictionary size is different, experiment effect has obvious difference, and discrimination of the present invention can be arrived 76.7%, average recognition rate reaches 71.8% under different dictionaries.
In order to verify the validity of proposition method of the present invention, table 1 gives the corresponding algorithm of the method for the invention and its The comparative experiments result of his method correspondence algorithm：
Table 1
Method LBP-top DTSA FDM The present invention
Discrimination 46.7% 39.7% 42.6% 71.4%
In order to make experimental result more convincing, the present invention, which is used, stays a check addition, and 20 experiments of progress are averaged Value.

Claims (2)

1. a kind of micro- expression recognition method based on the sparse transfer learning of voice dictionary, it is characterised in that the recognition methods includes Training stage and test phase；
The training stage comprises the following steps：
First, feature is extracted to voice domain and micro- expression domain；
Then, the characteristic extracted is grouped, the feature set in voice domain and micro- expression domain is divided into training set and survey Examination collection；
Then so that the data projection in voice domain and micro- expression domain obtains language simultaneously to a public space in public space The sparse dictionary of range and the sparse dictionary of micro- expression, the sparse dictionary of sparse dictionary and micro- expression domain to the voice domain enter The mutual reconstruct of row；
Afterwards, the iteration by certain number of times and optimization, respectively obtain the dictionary in voice domain and the dictionary in micro- expression domain, voice domain Projection matrix, the projection matrix in micro- expression domain, the restructuring matrix in voice domain, the restructuring matrix in micro- expression domain, voice domain it is dilute Sparse coefficient representing matrix, the sparse coefficient representing matrix in micro- expression domain；
The test phase comprises the following steps：
Test set for giving voice domain and micro- expression domain, the projection matrix obtained first by on-line training is to two characteristic of field Collection row projection；
Then, the dictionary and the dictionary in micro- expression domain in the voice domain obtained using training, two domains to projecting to public space Feature carries out sparse reconstruct, obtains the respective sparse coefficient representing matrix in two domains；
Finally, the sparse coefficient representing matrix in two domains is classified by machine recognition classic algorithm k nearest neighbor grader KNN Identification.
2. a kind of micro- expression recognition method based on the sparse transfer learning of voice dictionary according to claim 1, its feature It is, the k nearest neighbor grader KNN refers to the nearest neighbor classifier based on Euclidean distance, its method classified includes step such as Under：
First, feature extraction is carried out to voice domain and micro- expression domain, obtained Two characteristic sets, whereinThe feature of voice domain and a sample in micro- expression domain is represented respectively；mx,myRepresent respectively Voice domain and the intrinsic dimensionality in micro- expression domain, nx,nyThe sample size in voice domain and micro- expression domain is represented respectively；
Then, a pair of projection matrix W are found in trainingX,WY, by the Projection Character in two domains to public space, and pass through dictionary table Show sparse, i.e.,：
argmin W X , W Y , D X , D Y , S X , D Y | | W X T X - D X S X | | F 2 + | | W Y T Y - D Y S Y | | F 2
s . t . W X T W X = I X , W Y T W Y = I Y - - - ( 1 )
Wherein,What is represented is the projection matrix in voice domain,What is represented is the projection matrix in micro- expression domain,What is represented is the dictionary in voice domain,Represent the dictionary in micro- expression domain； Respectively Represent the sparse coefficient representing matrix in voice domain and micro- expression domain；D represents the projected dimensions that two domains project to public space； px,pyThe dictionary size in voice domain and micro- expression domain is represented respectively；Voice domain and micro- table are represented respectively The unit matrix in feelings domain；
Then, the data difference between not same area is reduced by reconstruct, representation is as follows：
argmin D X , D Y , V X , V Y , | | D X - D Y V X | | F 2 + | | D Y - D X V Y | | F 2 - - - ( 2 )
Wherein,The dictionary restructuring matrix of reconstructed voice collection and micro- expression collection is represented respectively, | | dxi||2≤ 1,||dyj||2≤1,||VX||1≤τ,||VY||1≤ τ, τ=0.001, dxi, dyjD is represented respectivelyX, DYIn column vector；
Finally, the object function of micro- expression recognition method based on the sparse transfer learning of voice dictionary is as follows：
J = argmin W X , W Y , D X , D Y , V X , V Y , S X , S Y | | W X T X - D X S X | | F 2 + | | W Y T Y - D Y S Y | | F 2 + | | D X - D Y V X | | F 2 + | | D Y - D X V Y | | F 2 - - - ( 3 )
Wherein,||dxi||2≤1,||dyj||2≤1,||Vx||1≤τ,||Vy||1≤ τ, | | Sx||1≤ σ,||Sy||1≤ σ, τ=0.001, σ=0.001；By the solution of object function, final W is obtainedX,WY,DX DY。
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