Paper Info
Title
Face recognition technology development with Gabor, PCA and SVM methodology under illumination normalization condition
Abstract
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.
Year
DOI
Venue
2018
10.1007/s10586-017-0806-7
Cluster Computing
Keywords
Field
DocType
Recognition, Illumination variation, Principal component analysis, Support vector machine, Illumination normalization
Computer vision,Facial recognition system,Normalization (statistics),Pattern recognition,Gabor wavelet,Computer science,Support vector machine,Feature extraction,Gaussian,Artificial intelligence,Histogram equalization,Principal component analysis
Journal
Volume
Issue
ISSN
21
1
1573-7543
Citations 
PageRank 
References 
1
0.37
21
Authors
6
Name
Order
Citations
PageRank
Meijing Li1507.60
Xiuming Yu2111.98
Keun Ho Ryu3814.39
Sanghyuk Lee427743.22
Sanghyuk Lee527743.22
Nipon Theera-umpon618430.59