Data driven Gabor wavelet design for face recognition

Linlin Shen, Li Bai, Zhen Ji

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

2 Citations (Scopus)

Abstract

In this paper we propose a novel data driven strategy for designing Gabor wavelets for face recognition. Each face image is represented through a multi-sensor scheme, which splits the 2D frequency plane into a number of channels and identifies the most significant units for extracting information. The representative units for a set of face images are then derived based on statistical analysis of these units. The locations of these units in the 2D frequency plane are then used to design the frequency and orientation of Gabor wavelets for face recognition. Once frequency and orientation are determined, the scale of a Gabor wavelet is determined by the sharpness of the filtered images. Two Gabor wavelet based face recognition algorithms are applied to demonstrate the advantages of the proposed strategy against conventional parameter settings. Experimental results show that the face recognition algorithms using the designed Gabor wavelets achieve better performance in terms of accuracy and efficiency. Since the strategy is based on the training data, it can be easily applied to designing Gabor wavelets for general pattern recognition task.

Original languageEnglish
Title of host publicationProceedings of the 2008 Chinese Conference on Pattern Recognition, CCPR 2008
Pages246-251
Number of pages6
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 Chinese Conference on Pattern Recognition, CCPR 2008 - Beijing, China
Duration: 22 Oct 200824 Oct 2008

Publication series

NameProceedings of the 2008 Chinese Conference on Pattern Recognition, CCPR 2008

Conference

Conference2008 Chinese Conference on Pattern Recognition, CCPR 2008
Country/TerritoryChina
CityBeijing
Period22/10/0824/10/08

Keywords

  • Face recognition
  • Gabor wavelet design

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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