Abstract
This paper proposes the use of curvelet entropy for classifying facial expressions from still images. The idea behind this work is that the expressions impose non-rigid motions on the face thereby changing the orientations of facial curves occurring due to different types of expressions. Hence a multiresolution transform like curvelet which refines its domain by using orientation information may be applied for the task of expression classification. Since similarity of facial expressions has earlier been studied using Gabor wavelet which uses filters oriented in different directions on specific feature points in images, the orientation selectivity and information content of curvelet subbands at specific facial points are used here. The information at selected facial points are gathered using the entropy of the corresponding pixel at various subbands. The proposed method is evaluated in the JAFFE and Cohn-Kanade databases without and with cross-validations. Experimental results show that the curvelet subband entropy at selected points may be used to form effective features for classifying facial expressions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bashyal, S., Venayagamoorthy, G.K.: Recognition of facial expressions using gabor wavelets and learning vector quantization. Engineering Applications of Artificial Intelligence 21(7), 1056–1064 (2008)
Candès, E., Demanet, L., Donoho, D., Ying, L.: Fast discrete curvelet transforms. Multiscale Modeling and Simulation 5(3), 861–899 (2006)
Candès, E.J., Donoho, D.L.: Curvelets, multiresolution representation and scaling laws. Wavelet Applications Signal Image Processing 4119, 1–12 (2000)
Chen, C., Jhang, Z.: Wavelet energy entropy as a new feature extractor for face recognition. In: International Conference on Image and Graphics, pp. 616–619 (2007)
Chen, F., Wang, Z., Xu, Z., Xiao, J.: Facial expression recognition based on wavelet energy distribution feature and neural network ensemble. In: Global Conference on Intelligent Systems, vol. 2, pp. 122–126 (2009)
Donoho, D., Duncan, M.: Digital curvelet transform: Strategy, implementation and experiments. Technical report, Stanford University (1999)
Fasel, B., Luettin, J.: Automatic facial expression analysis: A survey. Pattern Recognition 36, 259–275 (1999)
Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with gabor wavelets. In: International Conference on Automatic Face and Gesture Recognition, pp. 200–205 (1998)
Mandal, T., Wu, Q.M.J., Yuan, Y.: Curvelet based face recognition via dimension reduction. Signal Processing 89(12), 2345–2353 (2009)
Pantic, M., Rothkrantz, J.: Automatic analysis of facial expressions: State of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1424–1445 (2000)
Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: A comprehensive study. Image and Vision Computing 27(4), 803–816 (2008)
Starck, J., Candès, E.J., Donoho, D.L.: The curvelet transform for image denoising. IEEE Transactions on Image Processing 11(6), 670–684 (2000)
Celik, T., Ozkaramanli, H., Demirel, H.: Facial feature extraction using complex dual-tree wavelet transform. Computer Vision and Image Understanding 11(1), 229–246 (2008)
Zhang, Z., Lyons, M., Schuster, M., Akamatsu, S.: Comparison between geometry-based and gabor wavelets-based facial expression recognition using multi-layer perceptron. In: International Conference on Automatic Face and Gesture Recognition, pp. 454–459 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Saha, A., Wu, Q.M.J. (2010). Curvelet Entropy for Facial Expression Recognition. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15696-0_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-15696-0_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15695-3
Online ISBN: 978-3-642-15696-0
eBook Packages: Computer ScienceComputer Science (R0)