Copyright © 2007 The Institute of Electronics, Information and Communication Engineers
Regular section -- Papers -- Pattern Recognition |
Fusion-Based Age-Group Classification Method Using Multiple Two-Dimensional Feature Extraction Algorithms
1 The authors are with the Department of Science & Engineering, Waseda University, Tokyo, 169–8555 Japan. E-mail: ueki18{at}moegi.waseda.jp, 2 The author is with NEC Soft, Ltd., Tokyo, 136–8627 Japan.
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Abstract |
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An age-group classification method based on a fusion of different classifiers with different two-dimensional feature extraction algorithms is proposed. Theoretically, an integration of multiple classifiers can provide better performance compared to a single classifier. In this paper, we extract effective features from one sample image using different dimensional reduction methods, construct multiple classifiers in each subspace, and combine them to reduce age-group classification errors. As for the dimensional reduction methods, two-dimensional PCA (2DPCA) and two-dimensional LDA (2DLDA) are used. These algorithms are antisymmetric in the treatment of the rows and the columns of the images. We prepared the row-based and column-based algorithms to make two different classifiers with different error tendencies. By combining these classifiers with different errors, the performance can be improved. Experimental results show that our fusion-based age-group classification method achieves better performance than existing two-dimensional algorithms alone.
Key Words: 2DPCA, 2DLDA, age-group classification, face recognition, pattern recognition, z-score, min-max normalization, sum rule, product rule, max rule, min rule, classification combination
Manuscript received October 2, 2006. Manuscript revised January 29, 2007.