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Fusion-Based Age-Group Classification Method Using Multiple Two-Dimensional Feature Extraction Algorithms Kazuya UEKI Tetsunori KOBAYASHI Publication IEICE TRANSACTIONS on Information and Systems Vol.E90-D No.6 pp.923-934 Publication Date: 2007/06/01 Online ISSN: 1745-1361 DOI: 10.1093/ietisy/e90-d.6.923 Print ISSN: 0916-8532 Type of Manuscript: PAPER Category: Pattern Recognition Keyword: 2DPCA, 2DLDA, age-group classification, face recognition, pattern recognition, z-score, min-max normalization, sum rule, product rule, max rule, min rule, classification combination, Full Text: PDF(1.1MB)>>
Summary: 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. | ||||||||||
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