Cross-Modality 2D-3D Face Recognition via Multiview Smooth Discriminant Analysis Based on ELM
In recent years, 3D face recognition has attracted increasing attention from worldwide researchers. Rather than homogeneous face data, more and more applications require flexible input face data nowadays. In this paper, we propose a new approach for cross-modality 2D-3D face recognition (FR), which...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2014/584241 |
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| author | Yi Jin Jiuwen Cao Qiuqi Ruan Xueqiao Wang |
| author_facet | Yi Jin Jiuwen Cao Qiuqi Ruan Xueqiao Wang |
| author_sort | Yi Jin |
| collection | DOAJ |
| description | In recent years, 3D face recognition has attracted increasing attention from worldwide researchers. Rather than homogeneous face data, more and more applications require flexible input face data nowadays. In this paper, we propose a new approach for cross-modality 2D-3D face recognition (FR), which is called Multiview Smooth Discriminant Analysis (MSDA) based on Extreme Learning Machines (ELM). Adding the Laplacian penalty constrain for the multiview feature learning, the proposed MSDA is first proposed to extract the cross-modality 2D-3D face features. The MSDA aims at finding a multiview learning based common discriminative feature space and it can then fully utilize the underlying relationship of features from different views. To speed up the learning phase of the classifier, the recent popular algorithm named Extreme Learning Machine (ELM) is adopted to train the single hidden layer feedforward neural networks (SLFNs). To evaluate the effectiveness of our proposed FR framework, experimental results on a benchmark face recognition dataset are presented. Simulations show that our new proposed method generally outperforms several recent approaches with a fast training speed. |
| format | Article |
| id | doaj-art-927bd0f461714cd9ad87318d32e08ce7 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-927bd0f461714cd9ad87318d32e08ce72025-02-03T01:28:19ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552014-01-01201410.1155/2014/584241584241Cross-Modality 2D-3D Face Recognition via Multiview Smooth Discriminant Analysis Based on ELMYi Jin0Jiuwen Cao1Qiuqi Ruan2Xueqiao Wang3Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaInstitute of Information and Control, Hangzhou Dianzi University, Zhejiang 310018, ChinaBeijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaBeijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaIn recent years, 3D face recognition has attracted increasing attention from worldwide researchers. Rather than homogeneous face data, more and more applications require flexible input face data nowadays. In this paper, we propose a new approach for cross-modality 2D-3D face recognition (FR), which is called Multiview Smooth Discriminant Analysis (MSDA) based on Extreme Learning Machines (ELM). Adding the Laplacian penalty constrain for the multiview feature learning, the proposed MSDA is first proposed to extract the cross-modality 2D-3D face features. The MSDA aims at finding a multiview learning based common discriminative feature space and it can then fully utilize the underlying relationship of features from different views. To speed up the learning phase of the classifier, the recent popular algorithm named Extreme Learning Machine (ELM) is adopted to train the single hidden layer feedforward neural networks (SLFNs). To evaluate the effectiveness of our proposed FR framework, experimental results on a benchmark face recognition dataset are presented. Simulations show that our new proposed method generally outperforms several recent approaches with a fast training speed.http://dx.doi.org/10.1155/2014/584241 |
| spellingShingle | Yi Jin Jiuwen Cao Qiuqi Ruan Xueqiao Wang Cross-Modality 2D-3D Face Recognition via Multiview Smooth Discriminant Analysis Based on ELM Journal of Electrical and Computer Engineering |
| title | Cross-Modality 2D-3D Face Recognition via Multiview Smooth Discriminant Analysis Based on ELM |
| title_full | Cross-Modality 2D-3D Face Recognition via Multiview Smooth Discriminant Analysis Based on ELM |
| title_fullStr | Cross-Modality 2D-3D Face Recognition via Multiview Smooth Discriminant Analysis Based on ELM |
| title_full_unstemmed | Cross-Modality 2D-3D Face Recognition via Multiview Smooth Discriminant Analysis Based on ELM |
| title_short | Cross-Modality 2D-3D Face Recognition via Multiview Smooth Discriminant Analysis Based on ELM |
| title_sort | cross modality 2d 3d face recognition via multiview smooth discriminant analysis based on elm |
| url | http://dx.doi.org/10.1155/2014/584241 |
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