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Kernel Based Subspace Methods : Infrared vs Visible Face Recognition

Shahbe M. D., Hati S.

Machine Graphics and Vision

2009

Vol. 18, No. 1

47-66

This paper investigates the use of kernel theory in two well-known, linear-based subspace representations: Principle Component Analysis (PCA) and Fisher's Linear Discriminant Analysis (FLD). The kernel-based method provides subspaces of high-dimensional feature spaces induced by some nonlinear mappings. The focus of this work is to evaluate the performances of Kernel Principle Component Analysis (KPCA) and Kernel Fisher's Linear Discriminant Analysis (KFLD) for infrared (IR) and visible face recognition. The performance of the kernel-based subspace methods is compared with that of the conventional linear algorithms: PCA and FLD. The main contribution of this paper is the evaluation of the sensitivities of both IR and visible face images to illumination conditions, facial expressions and facial occlusions caused by eyeglasses using the kernel-based subspace methods.

Decision fusion based on voting scheme for IR and visible face recognition

Shahbe M. D., Hati S.

Machine Graphics and Vision

2008

Vol. 17, No. 4

403-424

In this paper we present an evaluation study of decision fusion strategies for infrared (IR) and visible face recognition. Several decision fusion methods based on a voting scheme (minimization, product and averaging) are discussed, and experiments for various conditions of probe and gallery sets are performed on two databases with paired IR and visible face imageries. The Eigenfaces and Fisherface classification techniques are used to extract the face features, and the performance of fusion methods on both classification approaches is discussed.