An Optimal Subspace Analysis for Face Recognition

In this chapter, we describe two tensor-based subspace analysis approaches (tensor ICA and tensor NMF) that can be used in many fields like face recognition and other biometric recognition. Section 10.1 gives the background and development of the two tensor-based subspace analysis approaches. Section 10.2 introduces tensor independent component analysis. Section 10.3 presents tensor nonnegative factorization. Section 10.4 discusses some potential applications of these two subspace analysis approaches in biometrics. Finally, we summarize this chapter in Section 10.5.

Download Full-text

Face Recognition and Semantic Features

Semantic Mining Technologies for Multimedia Databases ◽

10.4018/978-1-60566-188-9.ch003 ◽

2011 ◽

pp. 80-98

Author(s):

Huiyu Zhou ◽

Yuan Yuan ◽

Chunmei Shi

Keyword(s):

Face Recognition ◽

Singular Value Decomposition ◽

Eigenvalue Problem ◽

Singular Value ◽

Semantic Features ◽

Subspace Analysis ◽

Geometrical Properties ◽

The Face ◽

Tensor Subspace ◽

Value Decomposition

The authors present a face recognition scheme based on semantic features’ extraction from faces and tensor subspace analysis. These semantic features consist of eyes and mouth, plus the region outlined by three weight centres of the edges of these features. The extracted features are compared over images in tensor subspace domain. Singular value decomposition is used to solve the eigenvalue problem and to project the geometrical properties to the face manifold. They compare the performance of the proposed scheme with that of other established techniques, where the results demonstrate the superiority of the proposed method.

Download Full-text

KERNEL SUPERVISED DISCRIMINANT PROJECTION AND ITS APPLICATION FOR FACE RECOGNITION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800141356003x ◽

2013 ◽

Vol 27 (02) ◽

pp. 1356003 ◽

Cited By ~ 3

Author(s):

JIANGUO WANG

Keyword(s):

Face Recognition ◽

Experimental Results ◽

Subspace Method ◽

Subspace Analysis ◽

Face Images ◽

Discriminant Projection ◽

Kernel Mapping ◽

Discriminant Information

Subspace analysis is an effective approach for face recognition. In this paper, a novel subspace method, called kernel supervised discriminant projection (KSDP), is proposed for face recognition. In the proposed method, not only discriminant information with intrinsic geometric relations is preserved in subspace, but also complex nonlinear variations of face images are represented by nonlinear kernel mapping. Extensive experiments are performed to test and evaluate the new algorithm. Experimental results on three popular benchmark databases, FERET, Yale and AR, demonstrate the effectiveness of the proposed method, KSDP.

Download Full-text