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  • Improving the Performance of Facial Expression Recognition Using Dynamic, Subtle and Regional Features

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    Tjondronegoro224561-Accepted.pdf (915.7Kb)
    Author(s)
    Zhang, Ligang
    Tjondronegoro, Dian
    Griffith University Author(s)
    Tjondronegoro, Dian W.
    Year published
    2010
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    Abstract
    Human facial expression is a complex process characterized of dynamic, subtle and regional emotional features. State-of-the-art approaches on facial expression recognition (FER) have not fully utilized this kind of features to improve the recognition performance. This paper proposes an approach to overcome this limitation using patch-based 'salient' Gabor features. A set of 3D patches are extracted to represent the subtle and regional features, and then inputted into patch matching operations for capturing the dynamic features. Experimental results show a significant performance improvement of the proposed approach due to ...
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    Human facial expression is a complex process characterized of dynamic, subtle and regional emotional features. State-of-the-art approaches on facial expression recognition (FER) have not fully utilized this kind of features to improve the recognition performance. This paper proposes an approach to overcome this limitation using patch-based 'salient' Gabor features. A set of 3D patches are extracted to represent the subtle and regional features, and then inputted into patch matching operations for capturing the dynamic features. Experimental results show a significant performance improvement of the proposed approach due to the use of the dynamic features. Performance comparison with pervious work also confirms that the proposed approach achieves the highest CRR reported to date on the JAFFE database and a top-level performance on the Cohn-Kanade (CK) database.
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    Conference Title
    Lecture Notes in Computer Science
    Volume
    6444
    Issue
    PART 2
    DOI
    https://doi.org/10.1007/978-3-642-17534-3_72
    Copyright Statement
    © 2010 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, Information Systems
    Computer Science, Theory & Methods
    Publication URI
    http://hdl.handle.net/10072/390258
    Collection
    • Conference outputs

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