Boosted multi-resolution spatiotemporal descriptors for facial expression recognition

Authors:
Guoying Zhao;Matti Pietikäinen
Affiliations:
Machine Vision Group, Infotech Oulu and Department of Electrical and Information Engineering, P.O. Box 4500, University of Oulu, FI-90014 Oulu, Finland;Machine Vision Group, Infotech Oulu and Department of Electrical and Information Engineering, P.O. Box 4500, University of Oulu, FI-90014 Oulu, Finland
Venue:
Pattern Recognition Letters
Year:
2009

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Abstract

Recently, a spatiotemporal local binary pattern operator from three orthogonal planes (LBP-TOP) was proposed for describing and recognizing dynamic textures and applied to facial expression recognition. In this paper, we extend the LBP-TOP features to multi-resolution spatiotemporal space and use them for describing facial expressions. AdaBoost is utilized to learn the principal appearance and motion, for selecting the most important expression-related features for all the classes, or between every pair of expressions. Finally, a support vector machine (SVM) classifier is applied to the selected features for final recognition.