Paper Info
Title
Centralized Gradient Pattern For Face Recognition
Abstract
This paper proposes a novel face recognition approach using a centralized gradient pattern image and image covariance-based facial feature extraction algorithms, i.e. a two-dimensional principal component analysis and an alternative two-dimensional principal component analysis. The centralized gradient pattern image is obtained by AND operation of a modified center-symmetric local binary pattern image and a modified local directional pattern image, and it is then utilized as input image for the facial feature extraction based on image covariance. To verify the proposed face recognition method, the performance evaluation was carried out using various recognition algorithms on the Yale B, the extended Yale B and the CMU-PIE illumination databases. From the experimental results, the proposed method showed the best recognition accuracy compared to different approaches, and we confirmed that the proposed approach is robust to illumination variation.
Year
DOI
Venue
2013
10.1587/transinf.E96.D.538
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
DocType
Volume
centralized gradient pattern, local binary pattern, local directional pattern, face recognition
Journal
E96D
Issue
ISSN
Citations 
3
1745-1361
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Dong-Ju Kim16511.80
Sang-Heon Lee210522.48
Myoung-Kyu Sohn3337.17