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Person-Similarity Weighted Feature for Expression Recognition

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Computer Vision – ACCV 2007 (ACCV 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4844))

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Abstract

In this paper, a new method to extract person-independent expression feature based on HOSVD (Higher-Order Singular Value Decomposition) is proposed for facial expression recognition. With the assumption that similar persons have similar facial expression appearance and shape, person-similarity weighted expression feature is used to estimate the expression feature of the test person. As a result, the estimated expression feature can reduce the influence of individual caused by insufficient training data and becomes less person-dependent, and can be more robust to new persons. The proposed method has been tested on Cohn-Kanade facial expression database and Japanese Female Facial Expression (JAFFE) database. Person-independent experimental results show the efficiency of the proposed method.

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Yasushi Yagi Sing Bing Kang In So Kweon Hongbin Zha

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Tan, H., Zhang, YJ. (2007). Person-Similarity Weighted Feature for Expression Recognition. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_70

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  • DOI: https://doi.org/10.1007/978-3-540-76390-1_70

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76389-5

  • Online ISBN: 978-3-540-76390-1

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