Abstract
Recognizing facial expressions are a key part of human social interaction,and processing of facial expression information is largely automatic, but it is a non-trivial task for a computational system. The purpose of this work is to develop computational models capable of differentiating between a range of human facial expressions. Raw face images are examples of high dimensional data, so here we use some dimensionality reduction techniques: Linear Discriminant Analysis, Principal Component Analysis and Curvilinear Component Analysis. We also preprocess the images with a bank of Gabor filters, so that important features in the face images are identified. Subsequently the faces are classified using a Support Vector Machine. We show that it is possible to differentiate faces with a neutral expression from those with a smiling expression with high accuracy. Moreover we can achieve this with data that has been massively reduced in size: in the best case the original images are reduced to just 11 dimensions.
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References
Ekman, P., Friesen, W.V.: Constants across cultures in the face of the emotion. Journal of Personality and Social Psychology 17 (1971)
Batty, B., Taylor, M.J.: Early processing of the six basic facial emotional expressions. Cognitive Brain Research 17 (2003)
Demartines, P., Herault, D.J.: Curvilinear component analysis: A self-organizing neural network for nonlinear mapping of data sets. IEEE Transactions on Neural Networks 8(1), 148–154 (1997)
Grassberger, P., Proccacia, I.: Measuring the strangeness of strange attractors. Physica D 9 (1983)
Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using Gabor filters. Pattern Recognition 24(12) (1991)
Movellan, J.R.: Tutorial on Gabor Filters (2002)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001)
Fisher, R.A.: The use of mutliple measures in anatomical problems. Ann. Eugenics. 7, 179–188 (1936)
Belhumeur, Kriegman: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on patternAnalysis and Machine Intelligence 19(7), 711–720 (1997)
Zheng, D., Zhao, Y., Wang, J.: Features Extraction using A Gabor Filter Family. In: Proceedings of the sixth Lasted International conference, Signal and Image processing, Hawaii (2004)
Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency and orientation optimized by two dimensional visual cortical filters. Journal of Optical Society of America A 2(7) (1985)
Kulikowski: Theory of spatial position and spatial frequency relations in the receptive fields of simple cells in the visual cortex. Biological Cybernetics 43(3),187–198 (1982)
Smith, L.I.: Tutorial on Principal Component Analysis (2002)
Cortes, C., Vapnik, V.: Support Vector Networks. Machine Learning 20, 273–297 (1995)
Blanz, V., et al.: Comparison of view-based object recognition algorithms using realistic 3D models. In: Proc. Int. Conf. on Artificial Neural Networks, pp. 251–256 (1996)
Philips, P.J., et al.: The FERET evaluation methodology for face recognition algorithms. Image and Vision Computing 16(5), 295–306 (1998)
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Shenoy, A., Gale, T.M., Davey, N., Christiansen, B., Frank, R. (2008). Recognizing Facial Expressions: A Comparison of Computational Approaches. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_102
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DOI: https://doi.org/10.1007/978-3-540-87536-9_102
Publisher Name: Springer, Berlin, Heidelberg
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