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

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    Tjondronegoro224567-Accepted.pdf (178.7Kb)
    Author(s)
    Zhang, Ligang
    Tjondronegoro, Dian
    Griffith University Author(s)
    Tjondronegoro, Dian W.
    Year published
    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.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
    DOI
    https://doi.org/10.1007/978-3-642-10677-4_83
    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
    Publication URI
    http://hdl.handle.net/10072/390259
    Collection
    • Conference outputs

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