Semisupervised Kernel Marginal Fisher Analysis for Face Recognition
Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/981840 |
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| _version_ | 1832550063454289920 |
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| author | Ziqiang Wang Xia Sun Lijun Sun Yuchun Huang |
| author_facet | Ziqiang Wang Xia Sun Lijun Sun Yuchun Huang |
| author_sort | Ziqiang Wang |
| collection | DOAJ |
| description | Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm. |
| format | Article |
| id | doaj-art-54f8c454c30a4d72bcfe3c0349c20f1c |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-54f8c454c30a4d72bcfe3c0349c20f1c2025-02-03T06:07:51ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/981840981840Semisupervised Kernel Marginal Fisher Analysis for Face RecognitionZiqiang Wang0Xia Sun1Lijun Sun2Yuchun Huang3School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, ChinaSchool of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, ChinaSchool of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, ChinaSchool of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, ChinaDimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm.http://dx.doi.org/10.1155/2013/981840 |
| spellingShingle | Ziqiang Wang Xia Sun Lijun Sun Yuchun Huang Semisupervised Kernel Marginal Fisher Analysis for Face Recognition The Scientific World Journal |
| title | Semisupervised Kernel Marginal Fisher Analysis for Face Recognition |
| title_full | Semisupervised Kernel Marginal Fisher Analysis for Face Recognition |
| title_fullStr | Semisupervised Kernel Marginal Fisher Analysis for Face Recognition |
| title_full_unstemmed | Semisupervised Kernel Marginal Fisher Analysis for Face Recognition |
| title_short | Semisupervised Kernel Marginal Fisher Analysis for Face Recognition |
| title_sort | semisupervised kernel marginal fisher analysis for face recognition |
| url | http://dx.doi.org/10.1155/2013/981840 |
| work_keys_str_mv | AT ziqiangwang semisupervisedkernelmarginalfisheranalysisforfacerecognition AT xiasun semisupervisedkernelmarginalfisheranalysisforfacerecognition AT lijunsun semisupervisedkernelmarginalfisheranalysisforfacerecognition AT yuchunhuang semisupervisedkernelmarginalfisheranalysisforfacerecognition |