Sampling Gabor features for face recognition

Dang Hui Liu, Kin Man Lam, Lan Sun Shen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

The Gabor feature is effective for facial image representation. However, the dimension of a Gabor feature vector is very high so that the computation and memory requirements are prohibitively large. In this paper, we propose a method to determine the optimal position for extracting the Gabor feature. The sub-sampled positions of the feature points are determined by a mask generated from a set of training images by means of Principal Component Analysis (PCA). With the feature vector of reduced dimension, a subspace LDA is applied for face recognition. Experimental results show that the new sampling method is simple, and effective for both dimension reduction and image representation. The recognition rate based on our proposed scheme is also higher than that achieved using a regular sampling method in a face region.
Original languageEnglish
Title of host publicationProceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03
Pages924-927
Number of pages4
Volume2
DOIs
Publication statusPublished - 1 Dec 2003
Event2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03 - Nanjing, China
Duration: 14 Dec 200317 Dec 2003

Conference

Conference2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03
Country/TerritoryChina
CityNanjing
Period14/12/0317/12/03

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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