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Automatic facial expression recognition based on pixel‐pattern‐based texture feature
Author(s) -
Lu HuChuan,
Huang YingJie,
Chen YenWei
Publication year - 2010
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20245
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , pixel , gabor wavelet , computer vision , adaboost , feature (linguistics) , face (sociological concept) , grayscale , facial recognition system , principal component analysis , support vector machine , wavelet , feature vector , wavelet transform , discrete wavelet transform , linguistics , philosophy , social science , sociology
PCA, ICA, and Gabor wavelet are considered as the important and powerful face representation methods. In this article, we propose a new approach for face representation, which is called a pixel‐pattern‐based texture feature (PPBTF) and apply it to the real‐time facial expression recognition. A gray scale image is transformed into a pattern map where edges and lines are used for characterizing the facial texture information. Based on the pattern map, a feature vector is comprised of the numbers of the pixels belonging to each pattern. We use the image basis functions obtained by principal component analysis as the templates for pattern matching. Adaboost and Support Vector Machine are adopted to classify facial expression. Extensive experiments on the Cohn‐Kanade Database, PIE Database, and DUT Database illustrate that the PPBTF is quite effective and insensitive to illumination. The comparison with Gabor show the PPBTF is speedy. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 253–260, 2010