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 twodimensional 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.
Original language | English |
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Pages (from-to) | 923-934 |
Number of pages | 12 |
Journal | IEICE Transactions on Information and Systems |
Volume | E90-D |
Issue number | 6 |
DOIs | |
Publication status | Published - 2007 Jun |
Keywords
- 2DLDA
- 2DPCA
- Age-group classification
- Classification combination
- Face recognition pattern recognition
- Max rule
- Min rule
- Min-max normalization
- Produc rule
- Sum rule
- Z-score
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Artificial Intelligence