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Kernel Based Subspace Methods : Infrared vs Visible Face Recognition

Shahbe M. D., Hati S.

Machine Graphics and Vision

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2009

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Vol. 18, No. 1

47-66

EN

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.

2

Estimation of pose parameters from a set of least square objective functions

Hati S.

Machine Graphics and Vision

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2008

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Vol. 17, No. 3

299-312

EN

In this paper, we have attempted to solve the pose estimation problem for a 3-dimensional object by independently estimating the pose parameters through the minimization of a set of objective, functions, using Gauss approximation techniques for least squares optimization. In our implementation, the 3-D object is assumed to have three degrees of freedom on a flat surface, which is typical of automated visual inspection applications. However, the solution can be also extended to greater degrees of freedom. We have is shown that the pose can be estimated by only considering the x-coordinates of the known vertices in the projected space, but the same is not true if we consider the y-coordinates alone. We propose a set of modified objective functions from which it is possible to find the pose parameters. The parameters have been determined in noisy conditions under 20-dB and 40-dB SNR values and the robustness of the estimators is confirmed.

3

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

Shahbe M. D., Hati S.

Machine Graphics and Vision

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2008

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Vol. 17, No. 4

403-424

EN

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.