Title :
Discriminative filter based regression learning for facial expression recognition
Author :
Zizhao Zhang ; Yan Yan ; Hanzi Wang
Author_Institution :
Sch. of Inf. Sci. & Technol., Xiamen Univ., Xiamen, China
Abstract :
In this paper, we propose a novel discriminative filter based regression learning (DFRL) method, which can effectively remove irrelevant information while preserving useful information for facial expression recognition. DFRL integrates the filter technique and the linear analysis techniques (i.e., Linear Discriminant Analysis-LDA and Linear Ridge Regression-LRR) to obtain an effective image representation. Two steps are involved in DFRL: 1) The discriminative filters corresponding to different facial expressions are separately trained by optimizing the cost function of the two-class LDA, 2) LRR is used to extract valuable expressional information with high discriminability from the combined filtered images. Experimental results on several challenging datasets demonstrate the superior effectiveness and generalization ability of the proposed DFRL compared with other competing methods.
Keywords :
emotion recognition; face recognition; filtering theory; image representation; learning (artificial intelligence); regression analysis; DFRL method; LRR; combined filtered images; expressional information extraction; facial expression recognition; filter technique; image representation; linear analysis techniques; novel discriminative filter based regression learning method; two-class LDA; Cost function; Eyebrows; Face recognition; Gabor filters; Image representation; Learning systems; Filter design; facial expression recognition; regression learning;
Conference_Titel :
Image Processing (ICIP), 2013 20th IEEE International Conference on
Conference_Location :
Melbourne, VIC
DOI :
10.1109/ICIP.2013.6738246