Fusion-Based Age-Group Classification Method Using Multiple Two-Dimensional Feature Extraction Algorithms

• UEKI Kazuya

  Department of Science & Engineering, Waseda University

• KOBAYASHI Tetsunori

  Department of Science & Engineering, Waseda University

Abstract

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.

Journal

• IEICE Trans. Inf. & Syst., D

  IEICE Trans. Inf. & Syst., D 90 (6), 923-934, 2007-06-01

  The Institute of Electronics, Information and Communication Engineers

Keywords

• 2DPCA
• 2DLDA
• age-group classification
• face recognition
• pattern recognition
• z-score
• min-max normalization
• sum rule
• product rule
• max rule
• min rule
• classification combination

Details

• CRID: 1573950402323346304
• NII Article ID: 110007522148
• NII Book ID: AA10826272
• ISSN: 09168532
• Text Lang: en
• Data Source:
  • CiNii Articles