Facial expression recognition: A clustering-based approach

This paper describes a new clustering based feature extraction method for facial expression recognition. We demonstrate the effectiveness of this method and compare it with commonly used principal component analysis method and linear discriminant analysis method.

[1]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[2]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  A. Martínez,et al.  The AR face databasae , 1998 .

[4]  Alex Pentland,et al.  Coding, Analysis, Interpretation, and Recognition of Facial Expressions , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Jun Ohya,et al.  Recognizing multiple persons' facial expressions using HMM based on automatic extraction of significant frames from image sequences , 1997, Proceedings of International Conference on Image Processing.

[7]  Larry S. Davis,et al.  Recognizing Human Facial Expressions From Long Image Sequences Using Optical Flow , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Garrison W. Cottrell,et al.  EMPATH: Face, Emotion, and Gender Recognition Using Holons , 1990, NIPS.

[9]  Marian Stewart Bartlett,et al.  Classifying Facial Action , 1995, NIPS.

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

[11]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[12]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[13]  Kenji Mase,et al.  Recognition of Facial Expression from Optical Flow , 1991 .