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      Improving subspace learning for facial expression recognition using person dependent and geometrically enriched training sets.

      Neural Networks
      Algorithms, Artificial Intelligence, Cluster Analysis, Databases, Factual, Emotions, Eye, Facial Expression, Humans, Image Processing, Computer-Assisted, methods, Linear Models, Pattern Recognition, Automated, Principal Component Analysis, Recognition (Psychology), physiology, Reproducibility of Results

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          Abstract

          In this paper, the robustness of appearance-based subspace learning techniques in geometrical transformations of the images is explored. A number of such techniques are presented and tested using four facial expression databases. A strong correlation between the recognition accuracy and the image registration error has been observed. Although it is common-knowledge that appearance-based methods are sensitive to image registration errors, there is no systematic experiment reported in the literature. As a result of these experiments, the training set enrichment with translated, scaled and rotated images is proposed for confronting the low robustness of these techniques in facial expression recognition. Moreover, person dependent training is proven to be much more accurate for facial expression recognition than generic learning. Copyright © 2011 Elsevier Ltd. All rights reserved.

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