IEICE TRANSACTIONS on Information
A View Independent Video-Based Face Recognition Method Using Posterior Probability in Kernel Fisher Discriminant Space
Summary :
This paper presents a view independent video-based face recognition method using posterior probability in Kernel Fisher Discriminant (KFD) space. In practical environment, the view of faces changes dynamically. Robustness to view changes is required for video-based face recognition in practical environment. Since the view changes induce large non-linear variation, kernel-based methods are appropriate. We use KFD analysis to cope with non-linear variation. To classify image sequence, the posterior probability in KFD space is used. KFD analysis assumes that the distribution of each class in high dimensional space is Gaussian. This makes the computation of posterior probability in KFD space easy. The combination of KFD space and posterior probability of image sequence is the main contribution of the proposed method. The performance is evaluated by using two face databases. Effectiveness of the proposed method is shown by the comparison with the other feature spaces and classification methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E89-D No.7 pp.2150-2156
- Publication Date
- 2006/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e89-d.7.2150
- Type of Manuscript
- Special Section PAPER (Special Section on Machine Vision Applications)
- Category
- Face, Gesture, and Action Recognition
Authors
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Kazuhiro HOTTA, "A View Independent Video-Based Face Recognition Method Using Posterior Probability in Kernel Fisher Discriminant Space" in IEICE TRANSACTIONS on Information,
vol. E89-D, no. 7, pp. 2150-2156, July 2006, doi: 10.1093/ietisy/e89-d.7.2150.
Abstract: This paper presents a view independent video-based face recognition method using posterior probability in Kernel Fisher Discriminant (KFD) space. In practical environment, the view of faces changes dynamically. Robustness to view changes is required for video-based face recognition in practical environment. Since the view changes induce large non-linear variation, kernel-based methods are appropriate. We use KFD analysis to cope with non-linear variation. To classify image sequence, the posterior probability in KFD space is used. KFD analysis assumes that the distribution of each class in high dimensional space is Gaussian. This makes the computation of posterior probability in KFD space easy. The combination of KFD space and posterior probability of image sequence is the main contribution of the proposed method. The performance is evaluated by using two face databases. Effectiveness of the proposed method is shown by the comparison with the other feature spaces and classification methods.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.7.2150/_p
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@ARTICLE{e89-d_7_2150,
author={Kazuhiro HOTTA, },
journal={IEICE TRANSACTIONS on Information},
title={A View Independent Video-Based Face Recognition Method Using Posterior Probability in Kernel Fisher Discriminant Space},
year={2006},
volume={E89-D},
number={7},
pages={2150-2156},
abstract={This paper presents a view independent video-based face recognition method using posterior probability in Kernel Fisher Discriminant (KFD) space. In practical environment, the view of faces changes dynamically. Robustness to view changes is required for video-based face recognition in practical environment. Since the view changes induce large non-linear variation, kernel-based methods are appropriate. We use KFD analysis to cope with non-linear variation. To classify image sequence, the posterior probability in KFD space is used. KFD analysis assumes that the distribution of each class in high dimensional space is Gaussian. This makes the computation of posterior probability in KFD space easy. The combination of KFD space and posterior probability of image sequence is the main contribution of the proposed method. The performance is evaluated by using two face databases. Effectiveness of the proposed method is shown by the comparison with the other feature spaces and classification methods.},
keywords={},
doi={10.1093/ietisy/e89-d.7.2150},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - A View Independent Video-Based Face Recognition Method Using Posterior Probability in Kernel Fisher Discriminant Space
T2 - IEICE TRANSACTIONS on Information
SP - 2150
EP - 2156
AU - Kazuhiro HOTTA
PY - 2006
DO - 10.1093/ietisy/e89-d.7.2150
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2006
AB - This paper presents a view independent video-based face recognition method using posterior probability in Kernel Fisher Discriminant (KFD) space. In practical environment, the view of faces changes dynamically. Robustness to view changes is required for video-based face recognition in practical environment. Since the view changes induce large non-linear variation, kernel-based methods are appropriate. We use KFD analysis to cope with non-linear variation. To classify image sequence, the posterior probability in KFD space is used. KFD analysis assumes that the distribution of each class in high dimensional space is Gaussian. This makes the computation of posterior probability in KFD space easy. The combination of KFD space and posterior probability of image sequence is the main contribution of the proposed method. The performance is evaluated by using two face databases. Effectiveness of the proposed method is shown by the comparison with the other feature spaces and classification methods.
ER -
English
