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Research Of Facial Expression Recognition Method Based On Local Texture Feature Fusion

Posted on:2015-01-16Degree:DoctorType:Dissertation
Country:ChinaCandidate:D LiFull Text:PDF
GTID:1268330428983066Subject:Control theory and control engineering
Abstract/Summary:PDF Full Text Request
This paper studied the local texture feature of the expression recognition and the locationof key feature (such as eyes) was investigated on facial expression recognition in order torecognize the facial expressions based on the local feature. The purpose of this research is toimprove the facial recognition and the robustness of the algorithm. A novel facial expressionclassification based on the decision tree algorithm was provided in this paper, whichimproved the classification of facial expression recognition and provided an effectivemethod of data analysis and forecasts. The particle swarm optimization (PSO) algorithm wasused to solve the problem of traditional support vector machine (SVM) in order to improvethe accuracy of facial expression recognition system. For the static facial expression images,the location of the key facial feature, the face detection, the extraction and expression of thefeature of facial expression, and the classification were studied in this research. The mainresearch and innovative work are shown as follows:First, Gabor transform and principal component analysis (PCA) refactoring validationwere used to locate the eye area roughly and the two stage neighborhood arithmetic was usedto locate the pupil precisely. This method has the advantages of the non-iteratively and thesimple computation. Salient image extrema detection and PCA refactoring validation wereused to reduce the step of training sample computation under the requirement of locationaccuracy. The accuracy of eye location was more than98.6%as showed in the experimentresult.Second, Gabor filterbank was provided to extract the multi-scale and multi-directioncharacteristic of facial expression images. The features on the same scale in differentdirections of Gabor model syndrome were fused based on the fusion rules. The local binarypattern (LBP) operator was used to encode the Gabor model syndrome in. The sub-locksegmentation was used to improve the recognition of global image features. The image wassegmented into non-superposed elements with the equal area. Histograms of every element’sfusion features were combined to analyse the express image. The accuracy of facial expression recognition was93.42%as showed in the experiment. It has the advantage incomputation and recognition property compared with traditional method.Third, the PSO algorithm was provided in the traditional support vector machine withgrid search technique of classifying parameter to improve the efficiency of facial expressionrecognition system. The accuracy of recognition was improved to96.05%.Thedecision tree algorithm was used in classification of facial expression in this research whichprovided comparation of classification experiment and an effective method of data analysisand forecasts.Last, the main work of this research is summarized and the further work is discussed.This work was supported by Jilin Provincial Science and Technology development plankey Program (No.20071152), Graduate innovation project of Jilin University (No.20101027)and Jilin Youth Scientific Fund Project (No.20140520065JH).
Keywords/Search Tags:Facial expression recognition, Eye detection, Local binary pattern, Decision tree, Particle swarm optimization, Support vector machine
PDF Full Text Request
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