IEICE TRANSACTIONS on Information
Spatially Adaptive Logarithmic Total Variation Model for Varying Light Face Recognition
Summary :
In this letter, we propose an extension to the classical logarithmic total variation (LTV) model for face recognition under variant illumination conditions. LTV treats all facial areas with the same regularization parameters, which inevitably results in the loss of useful facial details and is harmful for recognition tasks. To address this problem, we propose to assign the regularization parameters which balance the large-scale (illumination) and small-scale (reflectance) components in a spatially adaptive scheme. Face recognition experiments on both Extended Yale B and the large-scale FERET databases demonstrate the effectiveness of the proposed method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.1 pp.155-158
- Publication Date
- 2013/01/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.155
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Biao WANG, Weifeng LI, Zhimin LI, Qingmin LIAO, "Spatially Adaptive Logarithmic Total Variation Model for Varying Light Face Recognition" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 1, pp. 155-158, January 2013, doi: 10.1587/transinf.E96.D.155.
Abstract: In this letter, we propose an extension to the classical logarithmic total variation (LTV) model for face recognition under variant illumination conditions. LTV treats all facial areas with the same regularization parameters, which inevitably results in the loss of useful facial details and is harmful for recognition tasks. To address this problem, we propose to assign the regularization parameters which balance the large-scale (illumination) and small-scale (reflectance) components in a spatially adaptive scheme. Face recognition experiments on both Extended Yale B and the large-scale FERET databases demonstrate the effectiveness of the proposed method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.155/_p
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@ARTICLE{e96-d_1_155,
author={Biao WANG, Weifeng LI, Zhimin LI, Qingmin LIAO, },
journal={IEICE TRANSACTIONS on Information},
title={Spatially Adaptive Logarithmic Total Variation Model for Varying Light Face Recognition},
year={2013},
volume={E96-D},
number={1},
pages={155-158},
abstract={In this letter, we propose an extension to the classical logarithmic total variation (LTV) model for face recognition under variant illumination conditions. LTV treats all facial areas with the same regularization parameters, which inevitably results in the loss of useful facial details and is harmful for recognition tasks. To address this problem, we propose to assign the regularization parameters which balance the large-scale (illumination) and small-scale (reflectance) components in a spatially adaptive scheme. Face recognition experiments on both Extended Yale B and the large-scale FERET databases demonstrate the effectiveness of the proposed method.},
keywords={},
doi={10.1587/transinf.E96.D.155},
ISSN={1745-1361},
month={January},}
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TY - JOUR
TI - Spatially Adaptive Logarithmic Total Variation Model for Varying Light Face Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 155
EP - 158
AU - Biao WANG
AU - Weifeng LI
AU - Zhimin LI
AU - Qingmin LIAO
PY - 2013
DO - 10.1587/transinf.E96.D.155
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2013
AB - In this letter, we propose an extension to the classical logarithmic total variation (LTV) model for face recognition under variant illumination conditions. LTV treats all facial areas with the same regularization parameters, which inevitably results in the loss of useful facial details and is harmful for recognition tasks. To address this problem, we propose to assign the regularization parameters which balance the large-scale (illumination) and small-scale (reflectance) components in a spatially adaptive scheme. Face recognition experiments on both Extended Yale B and the large-scale FERET databases demonstrate the effectiveness of the proposed method.
ER -
English
