Abstract
The present work investigates the performance of different multi-resolution transforms in the application of emotion recognition from facial images. Multi-resolution analysis of image provides frequency information along with time information in different scale, orientation and locations. The emotion information from facial images was being captured by different multiresolution algorithm such as Wavelet Transform, Curvelet Transform and Contourlet Transform. Wavelet transform mainly approximate frequency information along with time whereas curvelet transform is best to capture edges information with very few coefficients. Various statistical features obtained from different algorithms have been used to build reference model. The classification part was done using support vector machine (SVM) and K-Nearest Neighbor (KNN) classifier with JAFFE, a Japanese facial emotion database. The individual as well as comparative study of different algorithms was done successfully.
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References
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., Taylor, J.G.: Emotion recognition in human computer interaction. IEEE Signal Process. Magazine 20, 569–571 (2001)
Galateia, I.: Emotional Facial Expressions recognition & classification. MS thesis, Delft University of Technology, Delft, Netherland
Fasel, B., Luettin, J.: Automatic facial expression analysis: A survey. Pattern Recognition 36, 259–275 (2003)
Delac, K., Grqic, M., Bartlett, M.S.: Recent advances in face recognition. In-Tech Publication, Crosia (2008)
Chuang, Y., Yuning, H., Zhao, K.: The Method of Human Facial Expression Recognition Based on Wavelet Transformation Reducing the Dimension and Improved Fisher Discrimination. In: 3rd International Conference on Intelligent Networks and Intelligent Systems (ICINIS), pp. 43–47 (2010)
Zhi, R., Ruan, Q.: Robust Facial Expression Recognition Using Selected Wavelet Moment Invariants. In: WRI Global Congress on Intelligent Systems, GCIS 2009, pp. 508–512 (2009)
Muharram, M., Charkari, Moghaddam, N.: Multimodal information fusion application to human emotion recognition from face and speech. In: Multimedia Tools and Applications. LNCS, vol. 49(2), pp. 277–297. Springer, Heidelberg (1977)
Saha, A., Jonathan, Q.M.: Facial Expression Recognition using Curvelet based local binary patterns. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 2470–2473 (2010)
Wu, X., Zhao, J.: Curvelet feature extraction for face recognition and facial expression recognition. In: Sixth International Conference on Natural Computation (ICNC), pp. 1212–1216 (2010)
Lee, C.-C., Shih, C.-Y.: Facial expression recognition using contourlets and regularized discriminant analysis-based boosting algorithm. In: International Computer Symposium (ICS), pp. 1–5 (2010)
Shen, Y., Li, X., Ma, N.-W., Krishnan, S.: Parametric Time-Frequency Analysis and Its Applications in Music Classification. EURASIP Journal on Advances in Signal Processing 2010, Article ID 380349, 9 pages (2010)
Dai, D.-Q., Yan, H.: Wavelets and Face Recognition. I-Tech, Austria (2007)
Tzanetakis, G., Essl, G., Cook, P.: Audio Analysis using the Discrete Wavelet Transform. In: Proc. Conf. in Acoustics and Music Theory Applications, Skiathos, Greece (2001)
Curvelet Literature, http://www.curvelet.org
Lajevardi, S.M., Hussain, Z.M.: Contourlet structural similarity for facial expression recognition. In: IEEE International Conference on Acoustics Speech and Signal Processing, pp. 1118–1121 (2010)
Sumana, I., Islam, M., Zhang, D.S., Lu, G.: Content Based Image Retrieval Using Curvelet Transform. In: Proc. of IEEE International Workshop on Multimedia Signal Processing, Cairns, Queensland, Australia, pp. 11–16 (2008)
Esakkirajan, S., Veerakumar, T., Murugan, V.S., Sudhakar, R.: Fingerprint Compression Using Contourlet Transform and Multistage Vector Quantization. International Journal of Biological and Life Sciences 1, 2 (2005)
Do, M.N., Vetterli, M.: Pyramidal directional filter banks and curvelets. In: Proc. of IEEE Int. Conf. on Image Processing, Thessaloniki, Greece, vol. 3, pp. 158–161 (2001)
Do, M.N., Vetterli, M.: Contourlet Transform: An Efficient Directional Multiresolution Image Representation. IEEE Trans. on Image Processing (2001)
Lyons, M.J., Akamatsu, S., Kamachi, M., Goba, J.: Coding facial expressions with gabor wavelets. In: IEEE International Conference on Automatic Face and Gesture Recognition (1998)
Verma, G.K., Prasad, S., Bakul, G.: Robust Face Recognition using Curvelet Transform. In: International Conference on Communication, Computing & Security. ACM, Rourkela (2011)
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Verma, G.K., Tiwary, U.S., Rai, M.K. (2011). Facial Emotion Recognition Using Different Multi-resolution Transforms. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22720-2_49
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DOI: https://doi.org/10.1007/978-3-642-22720-2_49
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