Tensor-based feature representation with application to multimodal face recognition
Authors: Yu, H., Zhang, J.J. and Yang, X.
Journal: International Journal of Pattern Recognition and Artificial Intelligence
Volume: 25
Issue: 8
Pages: 1197-1217
ISSN: 0218-0014
DOI: 10.1142/S0218001411009081
Abstract:In this paper, a novel feature representation to multimodal face recognition is proposed, which possesses three properties: completeness, robustness and compactness. This feature descriptor allows all information of an object to be reproduced and its representation is invariant to rigid motion. In order to effectively take advantage of the proposed feature descriptor, we amend our previous ND-PCA scheme with multidirectional decomposition technique, and provide the estimation of the upper bound error of the amended classifier. It is proved to be linear optimal compared to other linear classifiers. To investigate the numerical performance of the presented feature descriptor, we apply it to both multiple modal and single modal samples, and the revised ND-PCA classifier is performed on the resulting feature representations. The experiments of verification and identification are carried out on two different gallery-probe face databases in order for the results to be evaluated by ROC and CMC curves independently. © 2011 World Scientific Publishing Company.
Source: Scopus
TENSOR-BASED FEATURE REPRESENTATION WITH APPLICATION TO MULTIMODAL FACE RECOGNITION
Authors: Yu, H., Zhang, J.J. and Yang, X.
Journal: INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Volume: 25
Issue: 8
Pages: 1197-1217
eISSN: 1793-6381
ISSN: 0218-0014
DOI: 10.1142/S0218001411009081
Source: Web of Science (Lite)
Tensor-based Feature Representation with Application to Multimodal Face Recognition
Authors: Yu, H., Zhang, J.J. and Yang, X.
Journal: International Journal of Pattern Recognition and Artificial Intelligence
Volume: 25
Pages: 1197-1217
DOI: 10.1142/S0218001411009081
Abstract:In this paper, a novel feature representation to multimodal face recognition is proposed, which possesses three properties: completeness, robustness and compactness. This feature descriptor allows all information of an object to be reproduced and its representation is invariant to rigid motion. In order to effectively take advantage of the proposed feature descriptor, we amend our previous ND-PCA scheme with multidirectional decomposition technique, and provide the estimation of the upper bound error of the amended classifier. It is proved to be linear optimal compared to other linear classifiers. To investigate the numerical performance of the presented feature descriptor, we apply it to both multiple modal and single modal samples, and the revised ND-PCA classifier is performed on the resulting feature representations. The experiments of verification and identification are carried out on two different gallery-probe face databases in order for the results to be evaluated by ROC and CMC curves independently.
http://www.worldscinet.com/ijprai/25/2508/S0218001411009081.html
Source: Manual
Preferred by: Jian Jun Zhang, Xiaosong Yang and Hongchuan Yu
Tensor-Based Feature Representation with Application to Multimodal Face Recognition.
Authors: Yu, H., Zhang, J.J. and Yang, X.
Journal: Int. J. Pattern Recognit. Artif. Intell.
Volume: 25
Pages: 1197-1217
DOI: 10.1142/S0218001411009081
Source: DBLP