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Centralized Gradient Pattern for Face Recognition

Title
Centralized Gradient Pattern for Face Recognition
Authors
Kim, Dong-JuLee, Sang-HeonShon, Myoung-Kyu
DGIST Authors
Lee, Sang-HeonShon, Myoung-Kyu
Issue Date
2013-03
Citation
IEICE Transactions on Information and Systems, E96D(3), 538-549
Type
Article
Article Type
Article
Keywords
AlgorithmsCenter-Symmetric Local Binary PatternsCentralized Gradient PatternDirectional Patterns (Antenna)Face RecognitionFace Recognition MethodsFacial Feature ExtractionFeature ExtractionIllumination VariationLocal Binary PatternLocal Binary PatternsLocal Directional PatternLocal Directional PatternsPrincipal Component AnalysisTwo-Dimensional (2-D)Two-Dimensional Principal Component Analysis
ISSN
1745-1361
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. Copyright © 2013 The Institute of Electronics, Information and Communication Engineers.
URI
http://hdl.handle.net/20.500.11750/5320
DOI
10.1587/transinf.E96.D.538
Publisher
Institute of Electronics, Information and Communication Engineers
Related Researcher
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Future Automotive Technology1. Journal Articles


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