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Facial expression recognition based on shape and texture
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DOI：10.1016/j.patcog.2008.08.034.png)
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摘要
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In this paper, an efficient method for human facial expression recognition is presented. We first propose a representation model for facial expressions, namely the spatially maximum occurrence model (SMOM), which is based on the statistical characteristics of training facial images and has a powerful representation capability. Then the elastic shape-texture matching (ESTM) algorithm is used to measure the similarity between images based on the shape and texture information. By combining SMOM and ESTM, the algorithm, namely SMOM-ESTM, can achieve a higher recognition performance level. The recognition rates of the SMOM-ESTM algorithm based on the AR database and the Yale database are 94.5% and 94.7%, respectively. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
Face recognition
Facial expression recognition
Elastic shape-texture matching
Spatially maximum occurrence model
Gabor wavelets
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7.6
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1.3W
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4.5W
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