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42 results on '"SONG, FENGXI"'

1. A Novel Supervised Dimensionality Reduction Algorithm for Online Image Recognition

Author

Song, Fengxi, Zhang, David, Chen, Qinglong, Yang, Jingyu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Chang, Long-Wen, editor, and Lie, Wen-Nung, editor

Published
2006
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4. A multiple maximum scatter difference discriminant criterion for facial feature extraction

Author

Song, Fengxi, Zhang, David, Mei, Dayong, and Guo, Zhongwei

Subjects
Face recognition (Psychology) -- Analysis
Abstract

Maximum scatter difference (MSD) discriminant criterion was a recently presented binary discriminant criterion for pattern classification that utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing small-sample-size problems. MSD classifiers based on this criterion have been quite effective on face-recognition tasks, but as they are binary classifiers, they are not as efficient on large-scale classification tasks. To address the problem, this paper generalizes the classification-oriented binary criterion to its multiple counterpart--multiple MSD (MMSD) discriminant criterion for facial feature extraction. The MMSD feature-extraction method, which is based on this novel discriminant criterion, is a new subspace-based feature-extraction method. Unlike most other subspace-based feature-extraction methods, the MMSD computes its discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. The MMSD is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the benchmark database, FERET, show that the MMSD outperforms state-of-the-art facial feature-extraction methods such as null space method, direct linear discriminant analysis (LDA), eigenface, Fisherface, and complete LDA. Index Terms--Face recognition, feature extraction, linear discriminant criterion.

Published
2007

10. Tensor Space

Author

Zhang, David, primary, Song, Fengxi, additional, Xu, Yong, additional, and Liang, Zhizhen, additional

Published
2009
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16. Overview

Author

Zhang, David, primary, Song, Fengxi, additional, Xu, Yong, additional, and Liang, Zhizhen, additional

Published
2009
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18. Book Summary

Author

Zhang, David, primary, Song, Fengxi, additional, Xu, Yong, additional, and Liang, Zhizhen, additional

Published
2009
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42. Large margin linear projection and face recognition

Author

Song, Fengxi, Yang, Jingyu, and Liu, Shuhai

Subjects
*FACE perception, *DISCRIMINANT analysis, *PATTERN perception, *MULTIVARIATE analysis
Abstract

Unlike linear discriminant analysis, the large margin linear projection (LMLP) classifier presented in this paper, which also roots in linear Fisher discriminant, takes full advantage of the singularity of within-class scatter matrix, and classifies projected points in one-dimensional space by itself. Theoretical analysis and experimental results both reveal that LMLP is well suited for high-dimensional small-sample pattern recognition problems such as face recognition. [Copyright &y& Elsevier]

Published
2004
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