Comparison of Region Based and Weighted Principal Component Analysis and Locally Salient ICA in Terms of Facial Expression Recognition
With the increasing applications of computing systems, recognizing accurate and application oriented human expressions, is becoming a challenging topic. The face is a highly attractive biometric trait for expression recognition because of its physiological structure and location. In this paper we proposed two different subspace projection methods that are the extensions of basis subspace projection methods and applied them successfully for facial expression recognition. Our first proposal is an improved principal component analysis for facial expression recognition in frontal images by using an extension of eigenspaces and we term this as WR-PCA (region based and weighted principal component analysis). Secondly we proposed locally salient Independent component analysis(LS-ICA) method for facial expression analysis. These two methods are extensively discussed in the rest of the paper. Experiments with Cohn-kanade database show that these techniques achieves an accuracy rate of 93% when using LS-ICA and 91.81% when WR-PCA and 83.05% when using normal PCA. Our main contribution here is that by performing WR-PCA, which is an extension of typical PCA and first investigated by us, we achieve a nearly similar result as LS-ICA which is a very well established technique to identify partial distortion.
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- 1.Frith, U., Baron-Cohen, S.: Perception in autistic children. In: Cohen, D.J., Donnellan, A.M. (eds.) Handbook of Autism and Pervasive Developmental Disorders, pp. 85–102. John Wiley, New York (1987)Google Scholar
- 2.Charlesworth, W.R., Kreutzer, M.A.: Facial expressions of infants and children. In: Ekman, P. (ed.) Darwin and Facial Expression: A Century of Research in Review, pp. 91–138. Academic Press, New York (1973)Google Scholar
- 3.Buciu, I., Kotropoulos, C., Pitas, I.: ICA and Gabor representation for facial expression recognition. In: International Conference on Image Processing, vol. 3, pp. II-855–II-858 (2003)Google Scholar
- 4.Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience (1991)Google Scholar
- 7.Pentland, A.P.: Recognition by parts. In: IEEE Proceedings of the First International Conference on Computer Vision, pp. 612–620 (1987)Google Scholar
- 8.Kolenda, T., Hansen, L.K., Larsen, J., Winther, O.: Independent Component Analysis for Understanding Multimedia Content. In: IEEE Workshop on Neural Networks for Signal Processing, pp. 757–766 (2002)Google Scholar
- 9.Zia Uddin, M., Lee, J.J., Kim, T.S.: An enhanced independent component based human facial expression recognition from video. IEEE Transactions on Consumer Electronics, 2216–2224 (2009)Google Scholar
- 10.Chen, F., Kotani, K.: Facial Expression Recognition by Supervised independent Component Analysis Using MAP Estimation. IEICE Transactions on Information and Systems, 341–350 (2008)Google Scholar
- 12.Kim, J., Choi, J., Yi, J.: Face Recognition Based on Locally Salient ICA Information. In: ECCV Workshop BioAW, pp. 1–9 (2004)Google Scholar
- 13.Lien, J.J.-J., Kanade, T., Cohn, J.F., Li, C.-C.: Automated Facial Expression Recognition Based on FACS Action Units. In: FG 1998, pp. 390–395 (1998)Google Scholar
- 14.Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive Database of Facial Expression Analysis. In: Proceedings of fourth IEEE International Conference on AFGR, Grenoble, France (2000)Google Scholar