Improved discriminant nearest feature space analysis for variable lighting face recognition

Shih Ming Huang, Jar Ferr Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

To improve the discriminant nearest feature space analysis (DNFSA) methods [6], in this paper, we propose an improved DNFSA (IDNFSA) algorithm to increase the robustness for variable lighting face recognition. The IDNFSA removes the mean of each image and attempts to minimize the within-class feature space (FS) distance and maximize the between-class FS distance simultaneously. In the IDNFSA, the first n eigenvectors are dropped and a generalized whitening transformation is suggested. In the recognition phase, the projected coefficients are classified by the nearest feature space rule with the ridge regression classification algorithm. Furthermore, to achieve higher accuracy, the illumination compensation is used. Experiments on the Extended Yale B (EYB) and FERET face databases reveal that the proposed approach outperforms the state-of-the-art methods for variable lighting face recognition.1.

Original languageEnglish
Title of host publication2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013
Pages2984-2987
Number of pages4
DOIs
Publication statusPublished - 2013 Sep 9
Event2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013 - Beijing, China
Duration: 2013 May 192013 May 23

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310

Other

Other2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013
CountryChina
CityBeijing
Period13-05-1913-05-23

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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