Neighborhood dependent approximation by nonlinear embedding for face recognition

Authors:
Ann Theja Alex;Vijayan K. Asari;Alex Mathew
Affiliations:
Computer Vision and Wide Area Surveillance Laboratory, Department of Electrical and Computer Engineering, University of Dayton, Dayton, Ohio;Computer Vision and Wide Area Surveillance Laboratory, Department of Electrical and Computer Engineering, University of Dayton, Dayton, Ohio;Computer Vision and Wide Area Surveillance Laboratory, Department of Electrical and Computer Engineering, University of Dayton, Dayton, Ohio
Venue:
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Year:
2011

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Abstract

Variations in pose, illumination and expression in faces make face recognition a difficult problem. Several researchers have shown that faces of the same individual, despite all these variations, lie on a complex manifold in a higher dimensional space. Several methods have been proposed to exploit this fact to build better recognition systems, but have not succeeded to a satisfactory extent. We propose a new method to model this higher dimensional manifold with available data, and use a reconstruction technique to approximate unavailable data points. The proposed method is tested on Sheffield (previously UMIST) database, Extended Yale Face database B and AT&T (previously ORL) database of faces. Our method outperforms other manifold based methods such as Nearest Manifold and other methods such as PCA, LDA Modular PCA, Generalized 2D PCA and super-resolution method for face recognition using nonlinear mappings on coherent features.