DocumentCode :
2692505
Title :
Weighted biased linear discriminant analysis for misalignment-robust facial expression recognition
Author :
Yan, Haibin ; Ang, Marcelo H., Jr. ; Poo, Aun Neow
Author_Institution :
Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore, Singapore
fYear :
2011
fDate :
9-13 May 2011
Firstpage :
3881
Lastpage :
3886
Abstract :
We investigate in this paper the problem of misalignment-robust facial expression recognition. To the best of our knowledge, this problem has not been formally addressed in the literature. Most existing facial expression recognition methods, however, can only work well when face images are well-aligned. In many real world applications such as human robot interaction and visual surveillance, it is still very challenging to obtain well-aligned face images for expression recognition due to currently imperfect vision techniques, especially under uncontrolled conditions. Motivated by the fact that interclass facial images with small differences are more easily mis-classified than those with large differences, we propose a biased linear discriminant analysis (BLDA) method by imposing large penalties on interclass samples with small differences and small penalties on those samples with large differences simultaneously, such that more discriminative features can be extracted for recognition. Moreover, we generate more virtually misaligned facial expression samples and assign different weights to them according to their occurrence probabilities in the testing phase to learn a weighted BLDA (WBLDA) feature space to extract misalignment-robust discriminative features for recognition. Experimental results on two widely used face databases are presented to show the efficacy of the proposed method.
Keywords :
emotion recognition; face recognition; feature extraction; statistical analysis; BLDA; face databases; face images; feature extraction; human robot interaction; misalignment-robust facial expression recognition; visual surveillance; weighted biased linear discriminant analysis; Databases; Face; Face recognition; Feature extraction; Image recognition; Testing; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation (ICRA), 2011 IEEE International Conference on
Conference_Location :
Shanghai
ISSN :
1050-4729
Print_ISBN :
978-1-61284-386-5
Type :
conf
DOI :
10.1109/ICRA.2011.5979870
Filename :
5979870
Link To Document :
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