Abstract
In this study, an expression manifold is constructed by Neighborhood Preserving Embedding (NPE) based on the expression semantic metric for a global representation of all possible facial expression images. On this learned manifold, images with semantic ‘similar’ expression are mapped onto nearby points whatever their lighting, pose and individual appearance are quite different. The proposed manifold extracts the universal expression feature and reveals the intrinsic semantic global structure and the essential relations of the expression data. Experimental results demonstrate the effectiveness of our approach.
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Xu, S., Jia, Y., Zhao, Y. (2007). Facial Expression Analysis on Semantic Neighborhood Preserving Embedding. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_107
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DOI: https://doi.org/10.1007/978-3-540-72393-6_107
Publisher Name: Springer, Berlin, Heidelberg
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