Gabor feature based face recognition using kernel methods

Linlin Shen, Li Bai

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

58 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. Kernel methods such as Kernel Principal Component Analysis (KPCA) and Kernel Discriminant Analysis (KDA) are 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 Kernel methods over classical Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Significant improvements are also observed when the Gabor filtered images are used for feature extraction instead of the original images. The Gabor + KDA method achieves 92% recognition accuracy using only 35 features of a face image.

Original languageEnglish
Title of host publicationProceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004
Pages170-176
Number of pages7
Publication statusPublished - 2004
Externally publishedYes
EventProceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004 - Seoul, Korea, Republic of
Duration: 17 May 200419 May 2004

Publication series

NameProceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition

Conference

ConferenceProceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004
Country/TerritoryKorea, Republic of
CitySeoul
Period17/05/0419/05/04

ASJC Scopus subject areas

  • Engineering (all)

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