Facial expression recognition based on local binary patterns and local fisher discriminant analysis

Automatic facial expression recognition is an interesting and challenging subject in signal processing, pattern recognition, artificial intelligence, etc. In this paper, a new method of facial expression recognition based on local binary patterns (LBP) and local Fisher discriminant analysis (LFDA) is presented. The LBP features are firstly extracted from the original facial expression images. Then LFDA is used to produce the low dimensional discriminative embedded data representations from the extracted high dimensional LBP features with striking performance improvement on facial expression recognition tasks. Finally, support vector machines (SVM) classifier is used for facial expression classification. The experimental results on the popular JAFFE facial expression database demonstrate that the presented facial expression recognition method based on LBP and LFDA obtains the best recognition accuracy of 90.7% with 11 reduced features, outperforming the other used methods such as principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP).

[1]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[3]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[4]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[5]  Michael J. Lyons,et al.  Coding facial expressions with Gabor wavelets , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[6]  Michael J. Lyons,et al.  Automatic Classification of Single Facial Images , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[8]  Takeo Kanade,et al.  Comprehensive database for facial expression analysis , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[9]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Bruce A. Draper,et al.  The CSU Face Identification Evaluation System: Its Purpose, Features, and Structure , 2003, ICVS.

[12]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[13]  Bruce A. Draper,et al.  The CSU Face Identification Evaluation System , 2005, Machine Vision and Applications.

[14]  M. Pietikäinen,et al.  FACIAL EXPRESSION RECOGNITION WITH LOCAL BINARY PATTERNS AND LINEAR PROGRAMMING , 2004 .

[15]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[16]  Ying-li Tian,et al.  Evaluation of Face Resolution for Expression Analysis , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[17]  M. Pietikäinen,et al.  Facial Expression Recognition with Local Binary Patterns and Linear Programming 1 , 2005 .

[18]  Shaogang Gong,et al.  Robust facial expression recognition using local binary patterns , 2005, IEEE International Conference on Image Processing 2005.

[19]  Shu Liao,et al.  Facial Expression Recognition using Advanced Local Binary Patterns, Tsallis Entropies and Global Appearance Features , 2006, 2006 International Conference on Image Processing.

[20]  Zhang Youwei,et al.  The contrast analysis of facial expression recognition by human and computer , 2006, 2006 8th international Conference on Signal Processing.

[21]  Zhang Youwei,et al.  Facial Expression Recognition Based on the Difference of Statistical Features , 2006, 2006 8th international Conference on Signal Processing.

[22]  Zhen Li,et al.  A Novel Feature Extraction Method for Facial Expression Recognition , 2006, JCIS.

[23]  Shinichi Nakajima,et al.  Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction , 2008, PAKDD.

[24]  Zhang Youwei,et al.  Facial expression recognition based on two dimensional feature extraction , 2008, 2008 9th International Conference on Signal Processing.

[25]  Shaogang Gong,et al.  Facial expression recognition based on Local Binary Patterns: A comprehensive study , 2009, Image Vis. Comput..

[26]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[27]  Takeo Kanade,et al.  Facial Expression Recognition , 2011, Handbook of Face Recognition.

[28]  Qiuqi Ruan,et al.  Orthogonal Tensor Neighborhood Preserving Embedding for facial expression recognition , 2011, Pattern Recognit..

[29]  Fernando De la Torre,et al.  Facial Expression Analysis , 2011, Visual Analysis of Humans.