Abstract
Robust facial expression recognition under facial occlusion condition is the main research orientation, which has important research significance. Many problems are caused by facial occlusion, not only missing facial expression information, but also bringing outliers or lots of noise. Aiming at the point, firstly, the face to be recognized is reconstructed using robust principal component analysis (RPCA); secondly, Eigenfaces and Fisherfaces are used to extract facial expression features respectively; finally, nearest neighbor method and support vector machine are used as classifiers. Facial expression recognition experiments are implemented in different occlusion conditions on Japanese female facial expression database (JAFFE). On the condition of big occlusion and small sample, RPCA algorithms gained better recognition results than many other methods, showing that this method based on RPCA is robust to kinds of facial occlusions.
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References
Wang, Y.H.: Face Recognition-Principle, Approach and Technology. Science Press, Beijing (2010)
Jiang, B., Jia, K.B., Yang, G.S.: Research Advance of Facial Expression Recognition. Computer Science 38, 25–31 (2011)
Kotsia, I., Buciu, I., Pitas, I.: An analysis of facial expression recognition under partial facial image occlusion. Image and Vision Computing 26, 1052–1067 (2008)
Kotsia, I., Zafeiriou, S., Pitas, I.: Novel multiclass classifiers based on the minimization of the within-class variance. Neural Networks 20, 14–34 (2009)
Towner, H., Slater, M.: Reconstruction and Recognition of Occluded Facial Expressions Using PCA. In: Paiva, A.C.R., Prada, R., Picard, R.W. (eds.) ACII 2007. LNCS, vol. 4738, pp. 36–47. Springer, Heidelberg (2007)
Xue, Y.L., Mao, X., Caleanu, D.C., et al.: Robust facial expression recognition under occlusion condition. Journal of Beijing University of Aeronautics and Astronautics 36, 429–433 (2010)
Zhang, J.M., Zhang, X.C.: Processing method of facial expression images under partial occlusion. Computer Engineering and Applications 47, 170–173 (2011)
Jin, Y.J., Shao, J.: Statistical Processing with Missing Data. China Statistics Press, Beijing (2009)
Huber, J.P., Ronchetti, E.M.: Robust Statistics, 2nd edn. John Wiley & Sons, Inc., Chichester (2009)
Lyons, M., Akamatsu, S., Kamachi, M., et al.: Coding facial expressions with Gabor wavelets. In: 3rd IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205. IEEE Press, New York (1998)
Fernando, T., Black, M.J.: A Framework for Robust Subspace Learning. Computer Vision 54, 117–142 (2003)
Lin, Z., Ganesh, A., Wright, J., et al.: Fast Convex Optimization Algorithms for Exact Recovery of a Corrupted Low-Rank Matrix.Technical report, UILU-ENG-09-2214 (2009)
Lin, Z., Chen, M., Wu, L., et al.: The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices. Technical report, UILU-ENG-09-2215 (2009)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. PAMI 19, 711–720 (1997)
Tipping, M.E., Bishop, C.M.: Probabilistic Principal Component Analysis. Technical report, Neural Computing Research Group, Aston University, UK (1997)
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Jiang, B., Jia, Kb. (2011). Research of Robust Facial Expression Recognition under Facial Occlusion Condition. In: Zhong, N., Callaghan, V., Ghorbani, A.A., Hu, B. (eds) Active Media Technology. AMT 2011. Lecture Notes in Computer Science, vol 6890. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23620-4_13
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DOI: https://doi.org/10.1007/978-3-642-23620-4_13
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