Facial Expression Recognition by Supervised Independent Component Analysis Using MAP Estimation

Fan CHEN  Kazunori KOTANI  

IEICE TRANSACTIONS on Information and Systems   Vol.E91-D   No.2   pp.341-350
Publication Date: 2008/02/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e91-d.2.341
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
facial expression recognition,  supervised independent component analysis,  fixed-point algorithm,  

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Permutation ambiguity of the classical Independent Component Analysis (ICA) may cause problems in feature extraction for pattern classification. Especially when only a small subset of components is derived from data, these components may not be most distinctive for classification, because ICA is an unsupervised method. We include a selective prior for de-mixing coefficients into the classical ICA to alleviate the problem. Since the prior is constructed upon the classification information from the training data, we refer to the proposed ICA model with a selective prior as a supervised ICA (sICA). We formulated the learning rule for sICA by taking a Maximum a Posteriori (MAP) scheme and further derived a fixed point algorithm for learning the de-mixing matrix. We investigate the performance of sICA in facial expression recognition from the aspects of both correct rate of recognition and robustness even with few independent components.