Please use this identifier to cite or link to this item:
Title: Face recognition technology development with Gabor, PCA and SVM methodology under illumination normalization condition
Authors: Meijing Li
Xiuming Yu
Keun Ho Ryu
Sanghyuk Lee
Nipon Theera-Umpon
Keywords: Computer Science
Issue Date: 9-Mar-2017
Abstract: © 2017 Springer Science+Business Media New York Face recognition is a challenging research field in computer sciences, numerous studies have been proposed by many researchers. However, there have been no effective solutions reported for full illumination variation of face images in the facial recognition research field. In this paper, we propose a methodology to solve the problem of full illumination variation by the combination of histogram equalization (HE) and Gaussian low-pass filter (GLPF). In order to process illumination normalization, feature extraction is applied with consideration of both Gabor wavelet and principal component analysis methods. Next, a Support Vector Machine classifier is used for face classification. In the experiments, illustration performance was compared with our proposed approach and the conventional approaches with three different kinds of face databases. Experimental results show that our proposed illumination normalization approach (HE_GLPF) performs better than the conventional illumination normalization approaches, in face images with the full illumination variation problem.
ISSN: 15737543
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.

Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.