Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22630
Title: Sampling Gabor features for face recognition
Authors: Liu, DH
Lam, KM 
Shen, LS
Issue Date: 2003
Source: Proceedings of the 2003 International Conference on Neural Networks and Signal Processing, 2003, 14-17 December 2003, Nanjing, v. 2, p. 924-927
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.
Keywords: Face recognition
Feature extraction
Image representation
Image sampling
Position measurement
Principal component analysis
Publisher: IEEE
ISBN: 0-7803-7702-8
DOI: 10.1109/ICNNSP.2003.1280751
Appears in Collections:Conference Paper

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