Kernel mutual subspace method for robust facial image recognition

Abstract

A multiple observation-based scheme (MObS) is described for robust facial recognition, and a novel object recognition method called kernel mutual subspace method (KMS) is proposed. The mutual sub-space method (MSM) proposed by (Maeda, et al., 1999) is a powerful method for recognizing facial images. However, its recognition accuracy is degraded when the data distribution has a nonlinear structure. To overcome this shortcoming we apply kernel principal component analysis (kPCP) to MSM. This paper describes theoretical aspects of the proposed method and presents the results of facial image recognition experiments.