Computer Science

Computer Science ›› 2018, Vol. 45 ›› Issue (10): 286-290.doi: 10.11896／j.issn.1002-137X.2018.10.053

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

LDA Facial Expression Recognition Algorithm Combining Optical Flow Characteristics with Gaussian

LIU Tao1, ZHOU Xian-chun2, YAN Xi-jun3   

  1. School of Information Mechanical & Electrical Engineering,Jiangsu Open University,Nanjing 210017,China 1
    School of Electronic and Information Engineering,Nanjing University of Information Science & Technology,Nanjing 210044,China 2
    College of Computer and Information,Hohai University,Nanjing 210098,China 3
  • Received:2017-08-08 Online:2018-11-05 Published:2018-11-05

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Abstract: This paper presented a new method for facial expression recognition,which uses dynamic optical flow features to describe the differences in facial expressions and improve the recognition rate of facial expression recognition.Firstly,the optical flow features between a peak emotion image and the neutral expression image are calculated.Then,the linear discriminant analysis (LDA) method is extended,and the Gaussian LDA method is used to map the optical flow features into eigenvector of facial expression image.Finally,multi-class support vector machine classifier is designed to achieve the classification and the recognition of facial expression.The experimental results on the JAFFE and CK facial expression databases show that the average recognition rates of the proposed method are more than 2% higher than three benchmark methods.

Key words: Expression recognition, Gaussian distribution, Linear discriminant analysis, Optical flow, Support vector machines

CLC Number: 

  • TP391
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