On a face recognition by the modified nonsingular discriminant analysis for a ubiquitous computing

Authors:
Jin Ok Kim;Kwang Hoon Jung;Chin Hyun Chung
Affiliations:
Faculty of Multimedia, Daegu Haany University, Gyeongsangbuk-do, Korea;Department of Information and Control Engineering, Kwangwoon University, Seoul, Korea;Department of Information and Control Engineering, Kwangwoon University, Seoul, Korea
Venue:
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
Year:
2006

Citing 5
Cited 0

Wavelets and subband coding

Wavelets and subband coding
Automatic Classification of Single Facial Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets

Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Dual-space linear discriminant analysis for face recognition

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an efficient face recognition by the modified nonsingular discriminant analysis for a ubiquitous computing. It is popular to extract discriminant features using Fisher linear discriminant analysis (LDA) for general face recognition. In this paper, we propose the modified nonsingular discriminant analysis in order to overcome the problem of small sample size and prone to be unrealizable due to the singularity of scatter matrices. The scatter matrix of transformed features is nonsingular. From the experiments on facial databases, we find that the modified nonsingular discriminant feature extraction achieves significant face recognition performance compared to other LDA-related methods for a limited range of sample sizes and class numbers. Also, recognition by the modified nonsingular discriminant analysis by using TMS320C6711 DSP Vision Board is set to highlight the advantages of our algorithm.