Kernel mutual subspace method for robust facial image recognition

Author

Sakano, Hitoshi ; Mukawa, Nmki

Author_Institution

NTT Data Corp., Japan

Volume

1

fYear

2000

fDate

2000

Firstpage

245

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

Keywords

face recognition; object recognition; principal component analysis; data distribution; experiments; facial image recognition; kernel mutual subspace method; kernel principal component analysis; multiple observation-based scheme; nonlinear structure; object recognition; Electronics packaging; Face recognition; Image recognition; Kernel; Noise reduction; Principal component analysis; Robustness; Space technology; Sprites (computer); Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on

Conference_Location

Brighton

Print_ISBN

0-7803-6400-7

Type

conf

DOI

10.1109/KES.2000.885803

Filename

885803