Abstract
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.
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Keywords
- Facial Expression
- Expression Recognition
- Facial Expression Recognition
- Locality Preserve Projection
- Hybrid Representation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Chang, WY., Chen, CS., Hung, YP. (2007). Analyzing Facial Expression by Fusing Manifolds. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_61
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DOI: https://doi.org/10.1007/978-3-540-76390-1_61
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