Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
An analysis of facial expression recognition under partial facial image occlusion
Image and Vision Computing
Reconstruction and Recognition of Occluded Facial Expressions Using PCA
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Novel multiclass classifiers based on the minimization of the within-class variance
IEEE Transactions on Neural Networks
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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.