Analyzing Facial Expression by Fusing Manifolds

10 years 3 months ago
Analyzing Facial Expression by Fusing Manifolds
Feature representation and classification are two major issues in facial expression analysis. In the past, most methods used either holistic or local representation for analysis. In essence, local information mainly focuses on the subtle variations of expressions and holistic representation stresses on global diversities. To take the advantages of both, a hybrid representation is suggested in this paper and manifold learning is applied to characterize global and local information discriminatively. Unlike some methods using unsupervised manifold learning approaches, embedded manifolds of the hybrid representation are learned by adopting a supervised manifold learning technique. To integrate these manifolds effectively, a fusion classifier is introduced, which can help to employ suitable combination weights of facial components to identify an expression. Comprehensive comparisons on facial expression recognition are included to demonstrate the effectiveness of our algorithm.
Wen-Yan Chang, Chu-Song Chen, Yi-Ping Hung
Added 06 Jun 2010
Updated 06 Jun 2010
Type Conference
Year 2007
Where ACCV
Authors Wen-Yan Chang, Chu-Song Chen, Yi-Ping Hung
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