Gabor wavelets and kernel direct discriminant analysis for face recognition

Linlin Shen, Li Bai

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

18 Citations (Scopus)

Abstract

A novel Gabor-Kernel face recognition method is proposed in this paper. This involves convolving a face image with a series of Gabor wavelets at different scales, locations, and orientations and extracting features from resulting Gabor filtered images. Kernel Discriminant Analysis (KDDA) is then applied to the feature vectors for dimension reduction as well as class separability enhancement. A database of 600 frontal-view face images from the FERET face database is used to test the method. Experimental results demonstrate the advantage of KDDA over other Kernel methods such as Kernel Principal Component Analysis (KPCA) and General Discriminant Analysis (GDA). Significant improvements are also observed when features are extracted from Gabor filtered images instead of the original images. A 94% accuracy has been observed for the novel Gabor + KDDA method on the FERET database using a simple classifier, which could be further improved by employing a more complex classifier and distance measurer.

Original languageEnglish
Title of host publicationProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
EditorsJ. Kittler, M. Petrou, M. Nixon
Pages284-287
Number of pages4
DOIs
Publication statusPublished - 2004
Externally publishedYes
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: 23 Aug 200426 Aug 2004

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume1
ISSN (Print)1051-4651

Conference

ConferenceProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
Country/TerritoryUnited Kingdom
CityCambridge
Period23/08/0426/08/04

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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