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,  

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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.