Nonparametric discriminant analysis for face recognition
In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multi-classifier integration. Traditional LDA-based methods suffer a fundamental limitation originating from the parametric nature of scatter matrices, which are based on the Gaussia...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/1721.1/52396 |
| _version_ | 1811079109125603328 |
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| author | Li, Zhifeng Lin, Dahua Tang, Xiaoou |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Li, Zhifeng Lin, Dahua Tang, Xiaoou |
| author_sort | Li, Zhifeng |
| collection | MIT |
| description | In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multi-classifier integration. Traditional LDA-based methods suffer a fundamental limitation originating from the parametric nature of scatter matrices, which are based on the Gaussian distribution assumption. The performance of these methods notably degrades when the actual distribution is Non-Gaussian. To address this problem, we propose a new formulation of scatter matrices to extend the two-class nonparametric discriminant analysis to multi-class cases. Then, we develop two more improved multi-class NDA-based algorithms (NSA and NFA) with each one having two complementary methods based on the principal space and the null space of the intra-class scatter matrix respectively. Comparing to the NSA, the NFA is more effective in the utilization of the classification boundary information. In order to exploit the complementary nature of the two kinds of NFA (PNFA and NNFA), we finally develop a dual NFA-based multi-classifier fusion framework by employing the over complete Gabor representation to boost the recognition performance. We show the improvements of the developed new algorithms over the traditional subspace methods through comparative experiments on two challenging face databases, Purdue AR database and XM2VTS database. |
| first_indexed | 2024-09-23T11:10:11Z |
| format | Article |
| id | mit-1721.1/52396 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:10:11Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/523962022-10-01T01:46:46Z Nonparametric discriminant analysis for face recognition Li, Zhifeng Lin, Dahua Tang, Xiaoou Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Lin, Dahua Lin, Dahua nonparametric discriminant analysis pattern recognition Face and gesture recognition classifier design and evaluation In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multi-classifier integration. Traditional LDA-based methods suffer a fundamental limitation originating from the parametric nature of scatter matrices, which are based on the Gaussian distribution assumption. The performance of these methods notably degrades when the actual distribution is Non-Gaussian. To address this problem, we propose a new formulation of scatter matrices to extend the two-class nonparametric discriminant analysis to multi-class cases. Then, we develop two more improved multi-class NDA-based algorithms (NSA and NFA) with each one having two complementary methods based on the principal space and the null space of the intra-class scatter matrix respectively. Comparing to the NSA, the NFA is more effective in the utilization of the classification boundary information. In order to exploit the complementary nature of the two kinds of NFA (PNFA and NNFA), we finally develop a dual NFA-based multi-classifier fusion framework by employing the over complete Gabor representation to boost the recognition performance. We show the improvements of the developed new algorithms over the traditional subspace methods through comparative experiments on two challenging face databases, Purdue AR database and XM2VTS database. Research Grants Council of the Hong Kong Special Administrative Region (Project CUHK 4190/01E, Project CUHK 4224/03E, and Project CUHK1/02C) 2010-03-08T20:52:46Z 2010-03-08T20:52:46Z 2009-02 2008-03 Article http://purl.org/eprint/type/JournalArticle 0162-8828 INSPEC Accession Number: 10476227 http://hdl.handle.net/1721.1/52396 Zhifeng Li, Dahua Lin, and Xiaoou Tang. “Nonparametric Discriminant Analysis for Face Recognition.” Pattern Analysis and Machine Intelligence, IEEE Transactions on 31.4 (2009): 755-761. © 2009 Institute of Electrical and Electronics Engineers 19229090 en_US http://dx.doi.org/10.1109/TPAMI.2008.174 IEEE Transactions on Pattern Analysis and Machine Intelligence Article is made available in accordance with the publisher’s policy and may be subject to US copyright law. Please refer to the publisher’s site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | nonparametric discriminant analysis pattern recognition Face and gesture recognition classifier design and evaluation Li, Zhifeng Lin, Dahua Tang, Xiaoou Nonparametric discriminant analysis for face recognition |
| title | Nonparametric discriminant analysis for face recognition |
| title_full | Nonparametric discriminant analysis for face recognition |
| title_fullStr | Nonparametric discriminant analysis for face recognition |
| title_full_unstemmed | Nonparametric discriminant analysis for face recognition |
| title_short | Nonparametric discriminant analysis for face recognition |
| title_sort | nonparametric discriminant analysis for face recognition |
| topic | nonparametric discriminant analysis pattern recognition Face and gesture recognition classifier design and evaluation |
| url | http://hdl.handle.net/1721.1/52396 |
| work_keys_str_mv | AT lizhifeng nonparametricdiscriminantanalysisforfacerecognition AT lindahua nonparametricdiscriminantanalysisforfacerecognition AT tangxiaoou nonparametricdiscriminantanalysisforfacerecognition |