Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE (2008)
Dec. 19, 2008 to Dec. 20, 2008
A feature fusion algorithm with application to facial expression recognition is presented. Firstly, the brows, eyes and mouth areas are segmented from the facial expression images, and are computed with Higher-order Local Auto-Correlation (HLAC) method, and the Weighted Principal Component Analysis (WPCA) is used to reduce dimensions secondly, in which the weights values are obtained according to facial expression measure system Face Action Coding System (FACS) in psychology. And finally minimum-distance classifier is used to recognize different expressions. Based on the CMU-PITTSBURGH AU-Coded Face Expression Image Database, the results show that the features fusing method is superior to PCA-Based method.
facial expression recognition, feature fusion, higher-order local auto-correlations (HLAC), weighted principal component analysis (WPCA)
Y. Wang, Z. Wang, F. Chen, Z. Xu and F. Liu, "A Facial Expression Recognition Algorithm Based on Feature Fusion," 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application. PACIIA 2008(PACIIA), Wuhan, 2008, pp. 381-385.