Title :
Kernel mutual subspace method for robust facial image recognition
Author :
Sakano, Hitoshi ; Mukawa, Nmki
Author_Institution :
NTT Data Corp., Japan
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;
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
DOI :
10.1109/KES.2000.885803
