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Facial Expression Recognition Using a New Image Representation and Multiple Feature Fusion

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Intelligent Science and Intelligent Data Engineering (IScIDE 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7751))

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Abstract

This paper proposes a novel method for facial expression recognition using a new image representation and multiple feature fusion. First, the new image representation is derived from the normalized hybrid color space, by principal component analysis (PCA) followed by Fisher linear discriminant analysis (FLDA). Second, multi-scale local phase quantization (LPQ) features and patch-based Gabor features are applied to the new image representation and gray-level image, respectively, to extract multiple feature sets. Finally, due to the complementary characteristic between the new image representation and gray-level image, combining the classification results of multiple feature sets at the score level can improve recognition performance further. Experiments on Multi-PIE show that the proposed method achieves state-of-the-art performance for facial expression recognition.

This work is supported by the National Natural Science Foundation of China under grant no. 61271330.

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Liu, Z., Wu, W., Tao, Q., Yang, J. (2013). Facial Expression Recognition Using a New Image Representation and Multiple Feature Fusion. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_54

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  • DOI: https://doi.org/10.1007/978-3-642-36669-7_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36668-0

  • Online ISBN: 978-3-642-36669-7

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