Analyzing Facial Expression by Fusing Manifolds [chapter]

Wen-Yan Chang, Chu-Song Chen, Yi-Ping Hung
Computer Vision – ACCV 2007  
Feature representation and classi cation 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
more » ... ion 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 classi er 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.
doi:10.1007/978-3-540-76390-1_61 dblp:conf/accv/ChangCH07 fatcat:rku4jwolb5eq3nyndxq2ahdhhi