Improved facial expression recognition via uni-hyperplane classification
Data(s) |
16/06/2012
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Resumo |
Large margin learning approaches, such as support vector machines (SVM), have been successfully applied to numerous classification tasks, especially for automatic facial expression recognition. The risk of such approaches however, is their sensitivity to large margin losses due to the influence from noisy training examples and outliers which is a common problem in the area of affective computing (i.e., manual coding at the frame level is tedious so coarse labels are normally assigned). In this paper, we leverage the relaxation of the parallel-hyperplanes constraint and propose the use of modified correlation filters (MCF). The MCF is similar in spirit to SVMs and correlation filters, but with the key difference of optimizing only a single hyperplane. We demonstrate the superiority of MCF over current techniques on a battery of experiments. |
Formato |
application/pdf |
Identificador | |
Publicador |
IEEE |
Relação |
http://eprints.qut.edu.au/49809/1/Improved-Facial-Expression.pdf DOI:10.1109/CVPR.2012.6247973 Chew, Sien Wei, Lucey, Simon, Lucey, Patrick J., & Sridharan, Sridha (2012) Improved facial expression recognition via uni-hyperplane classification. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Rhode Island Convention Center, Providence, Rhode Island, pp. 2554-2561. |
Direitos |
Copyright 2012 [Please consult the author] |
Fonte |
School of Electrical Engineering & Computer Science; Information Security Institute; Science & Engineering Faculty |
Palavras-Chave | #080100 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING #080104 Computer Vision #080109 Pattern Recognition and Data Mining #support vector machines #correlation filters #facial expression recognition |
Tipo |
Conference Paper |