Face recognition based on gabor features and two-dimensional PCA

Yi Chun Lee, Chin-Hsing Chen

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

2 Citations (Scopus)

Abstract

This paper presents a new face recognition method based on Two-Dimensional Principal Component Analysis (2DPCA) and Gabor filters. In the method, an original image is convolved with 40 Gabor filters corresponding to various orientations and scales to give its Gabor representation. Then, the Gabor representation is analyzed by the 2DPCA in which the eigenvectors are computed using the Gabor image covariance matrix without matrix to vector conversion. Experiments based on the ORL database were then performed to compare the recognition rate between the PCA, the 2DPCA, the 2DPCA+GF and the 2DPCA+MGF. We find that the recognition rate using 1-norm distance measure is better in the 2DPCA+MGF method. It achieves 98.5% recognition rate by using 25 principal components of 2DPCA using the 1-norm distance classifier.

Original languageEnglish
Title of host publicationProceedings - 2008 4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2008
Pages572-576
Number of pages5
DOIs
Publication statusPublished - 2008 Oct 22
Event2008 4th International Conference on Intelligent Information Hiding and Multiedia Signal Processing, IIH-MSP 2008 - Harbin, China
Duration: 2008 Aug 152008 Aug 17

Publication series

NameProceedings - 2008 4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2008

Other

Other2008 4th International Conference on Intelligent Information Hiding and Multiedia Signal Processing, IIH-MSP 2008
CountryChina
CityHarbin
Period08-08-1508-08-17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Signal Processing

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