Share Email Print

Proceedings Paper

Facial expression recognition with facial parts based sparse representation classifier
Author(s): Ruicong Zhi; Qiuqi Ruan
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960O (30 October 2009); doi: 10.1117/12.831228
Show Author Affiliations
Ruicong Zhi, Beijing Jiaotong Univ. (China)
Qiuqi Ruan, Beijing Jiaotong Univ. (China)

Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?