Facial Expression Analysis on Semantic Neighborhood Preserving Embedding

Authors:
Shuang Xu;Yunde Jia;Youdong Zhao
Affiliations:
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, P.R. China;School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, P.R. China;School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, P.R. China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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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.