Neighborhood Dependent Approximation by Nonlinear Embedding for Face Recognition
- Cite this paper as:
- Alex A.T., Asari V.K., Mathew A. (2011) Neighborhood Dependent Approximation by Nonlinear Embedding for Face Recognition. In: Maino G., Foresti G.L. (eds) Image Analysis and Processing – ICIAP 2011. ICIAP 2011. Lecture Notes in Computer Science, vol 6978. Springer, Berlin, Heidelberg
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.
KeywordsFace Recognition Manifold Learning Nonlinear Embedding
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