Selecting, Optimizing and Fusing `Salient' Gabor Features for Facial Expression Recognition

Authors:
Ligang Zhang;Dian Tjondronegoro
Affiliations:
Faculty of Science and Techonogly, Queensland University of Techonogly, Brisbane, Australia 4000;Faculty of Science and Techonogly, Queensland University of Techonogly, Brisbane, Australia 4000
Venue:
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Year:
2009

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Abstract

This paper describes a novel framework for facial expression recognition from still images by selecting, optimizing and fusing `salient' Gabor feature layers to recognize six universal facial expressions using the K nearest neighbor classifier. The recognition comparisons with all layer approach using JAFFE and Cohn-Kanade (CK) databases confirm that using `salient' Gabor feature layers with optimized sizes can achieve better recognition performance and dramatically reduce computational time. Moreover, comparisons with the state of the art performances demonstrate the effectiveness of our approach.