Abstract

In the last decade, facial expression recognition has attracted more and more interest of researchers in the computer vision community. Facial expressions are a form of verbal communication, used to exchange social and emotional information in human-human-interaction. By detecting the expression of a human and reacting proactively, many applications could benefit from automatic facial expression recognition systems, e.g. human-computer-interfaces or security systems. Further applications for expression recognition lie in driver safety and social sciences. In order to use facial expression recognition systems in real-world situations, it is essential to recognize expressions not only from front face images, but also from images containing faces with pose variations. In This work a new feature extraction technique has introduced from still images using PCA on curvelet domain which has been evaluated on a well-known databases. Curvelet Transform has better directional and edge representation abilities, inspired by these attractive attributes of curvelets, we decomposed images into its curvelet subbands and apply PCA (Principal Component Analysis) on the selected subbands in order to create a representative feature set.

References

• B. Fasel and J. Luettin, “Automatic facial expression analysis: A survey,” Pattern Recognition, vol. 36, pp. 259–275, 1999.
• C. Shan, S. Gong, and P.W. McOwan, “Facial expression recognition based on local binary patterns: A comprehensive study,” Image and Vision Computing, vol. 27(4), pp. 803–816, 2008.
• Y. Tian, T. Kanade, and J. Cohn, Handbook of Face Recognition, chapter 11, Springer, 2005.
• M.S. Bartlett, G. Littlewort, M. Frank, C. Lainscsek, I. Fasel, and J. Movellan, “Recognizing facial expression: Machine learning and application to spotaneous behavior,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, vol. 2, pp. 568–573.
• M.J. Lyons, J. Budynek, and S. Akamatsu, “Automatic classification of single facial images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21(12), pp. 1357–1362, 1999.
• T. Mandal, A. Majumdar, and Q.M.J. Wu, “Face recognition by curvelet based feature extraction,” in International Conference on Image Analysis and Recognition, LNCS, 2007, vol. 4633, pp. 806–817.
• A.A. Mohammed, R. Minhas, Q.M.J. Wu, and M.A. Sid-Ahmed, “A novel technique for human face recognition using nonlinear curvelet feature subspace,” in International Conference on Image Analysis and Recognition, LNCS, 2009, vol. 5627, pp. 512–521.
• T. Ojala, M. Pietikainen, and D. Harwood, “A comparative study of tex- ¨ ture measures with classification based on featured distribution,” Pattern Recognition, vol. 29 (1), pp. 51–59, 1996.
• S. Liao, W. Fan, C.S. Chung, and D.Y. Yeung, “Facial expression recognition using advanced local binary patterns, tsallis entropies and global appearance features,” in IEEE International Conference on Image Processing, 2006, pp. 665–668.
• M.J. Lyons, S. Akamatsu, M. Kamachi, and J. Goba, “Coding facial expressions with gabor wavelets,” in IEEE International Conference on Automatic Face and Gesture Recognition, 1998, pp. 200–205.