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Centralized Gradient Pattern for Face Recognition
- Centralized Gradient Pattern for Face Recognition
- Kim, Dong Ju; Lee, Sang Heon; Sohn, Myeong Kyu
- DGIST Authors
- Lee, Sang Heon; Sohn, Myeong Kyu
- Issue Date
- IEICE Transactions on Information and Systems, E96D(3), 538-549
- Article Type
- Algorithms; Center-Symmetric Local Binary Patterns; Centralized Gradient Pattern; Directional Patterns (Antenna); Face Recognition; Face Recognition Methods; Facial Feature Extraction; Feature Extraction; Illumination Variation; Local Binary Pattern; Local Binary Patterns; Local Directional Pattern; Local Directional Patterns; Principal Component Analysis; Two-Dimensional (2-D); Two-Dimensional Principal Component Analysis
- 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.
- Institute of Electronics, Information and Communication Engineers
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