Facial expression recognition with facial parts based sparse representation classifier

Ruicong Zhi; Qiuqi Ruan

doi:10.1117/12.831228

30 October 2009 Facial expression recognition with facial parts based sparse representation classifier

Ruicong Zhi, Qiuqi Ruan

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74960O (2009) https://doi.org/10.1117/12.831228
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Facial expressions play important role in human communication. The understanding of facial expression is a basic requirement in the development of next generation human computer interaction systems. Researches show that the intrinsic facial features always hide in low dimensional facial subspaces. This paper presents facial parts based facial expression recognition system with sparse representation classifier. Sparse representation classifier exploits sparse representation to select face features and classify facial expressions. The sparse solution is obtained by solving l¹ -norm minimization problem with constraint of linear combination equation. Experimental results show that sparse representation is efficient for facial expression recognition and sparse representation classifier obtain much higher recognition accuracies than other compared methods.

Citation Download Citation

Ruicong Zhi and Qiuqi Ruan "Facial expression recognition with facial parts based sparse representation classifier", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960O (30 October 2009); https://doi.org/10.1117/12.831228

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