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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Donato, G., Bartlett, M., Hager, J., Ekman, P., Sejnowski, J.: Classifying facial actions. IEEE Trans. Pattern Analysis and Machine Intelligence 21(10), 974–989 (1999)
Moore, S., Bowden, R.: Local binary patterns for multi-view facial expression recognition. Computer Vision and Image Understanding 115, 541–558 (2011)
Yang, J., Liu, C., Zhang, L.: Color space normalization: Enhancing the discriminating power of color spaces for face recognition. Pattern Recognition 43, 1454–1466 (2010)
Liu, Z., Yang, J., Liu, C.: Extracting multiple features in the CID color space for face recognition. IEEE Trans. on Image Processing 19(9), 2502–2509 (2010)
Choi, J.Y., Ro, Y.M., Platanioits, K.N.: Boosting color feature selection for color face recognition. IEEE Trans. on Image Processing 20(5), 1425–1434 (2011)
Lee, S.H., Choi, J.Y., Ro, Y.M., Platanioits, K.N.: Local color vector binary patterns from multichannel face images for face recognition. IEEE Trans. on Image Processing 21(4), 2347–2353 (2012)
Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. Image and Vision Computing 28, 807–813 (2010)
Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: Proc. ICISP (2008)
Chan, C.H., Kittler, J., Poh, N., Ahonen, T., Pietikäinen, M.: Multiscale local phase quantisation histogram discriminant analysis with score normalisation for robust face recognition. In: Proc. ICCV Workshops, pp. 633–640 (2009)
Zhang, W., Shan, S., Chen, X., Gao, W.: Local Gabor binary pattern histogram sequence (LGBPHS): a non-statistical model for face representation and recognition. In: Proc. ICCV, pp. 786–791 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)