Selecting, Optimizing and Fusing 'Salient' Gabor Features for Facial Expression Recognition
File version
Accepted Manuscript (AM)
Author(s)
Zhang, Ligang
Tjondronegoro, Dian
Griffith University Author(s)
Year published
2009
Metadata
Show full item recordAbstract
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.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.
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Conference Title
Lecture Notes in Computer Science
Volume
5863
Issue
PART 1
Copyright Statement
© 2009 Springer-Verlag Berlin Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
Subject
Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods