IEICE TRANSACTIONS on Information
Learning Convolutional Domain-Robust Representations for Cross-View Face Recognition
Summary :
This paper proposes to obtain high-level, domain-robust representations for cross-view face recognition. Specially, we introduce Convolutional Deep Belief Networks (CDBN) as the feature learning model, and an CDBN based interpolating path between the source and target views is built to model the correlation of cross-view data. The promising results outperform other state-of-the-art methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.12 pp.3239-3243
- Publication Date
- 2014/12/01
- Publicized
- 2014/09/08
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDL8095
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Xue CHEN
Chinese Academy of Sciences
Chunheng WANG
Chinese Academy of Sciences
Baihua XIAO
Chinese Academy of Sciences
Song GAO
Chinese Academy of Sciences
Keyword
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Xue CHEN, Chunheng WANG, Baihua XIAO, Song GAO, "Learning Convolutional Domain-Robust Representations for Cross-View Face Recognition" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 12, pp. 3239-3243, December 2014, doi: 10.1587/transinf.2014EDL8095.
Abstract: This paper proposes to obtain high-level, domain-robust representations for cross-view face recognition. Specially, we introduce Convolutional Deep Belief Networks (CDBN) as the feature learning model, and an CDBN based interpolating path between the source and target views is built to model the correlation of cross-view data. The promising results outperform other state-of-the-art methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDL8095/_p
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@ARTICLE{e97-d_12_3239,
author={Xue CHEN, Chunheng WANG, Baihua XIAO, Song GAO, },
journal={IEICE TRANSACTIONS on Information},
title={Learning Convolutional Domain-Robust Representations for Cross-View Face Recognition},
year={2014},
volume={E97-D},
number={12},
pages={3239-3243},
abstract={This paper proposes to obtain high-level, domain-robust representations for cross-view face recognition. Specially, we introduce Convolutional Deep Belief Networks (CDBN) as the feature learning model, and an CDBN based interpolating path between the source and target views is built to model the correlation of cross-view data. The promising results outperform other state-of-the-art methods.},
keywords={},
doi={10.1587/transinf.2014EDL8095},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Learning Convolutional Domain-Robust Representations for Cross-View Face Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 3239
EP - 3243
AU - Xue CHEN
AU - Chunheng WANG
AU - Baihua XIAO
AU - Song GAO
PY - 2014
DO - 10.1587/transinf.2014EDL8095
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2014
AB - This paper proposes to obtain high-level, domain-robust representations for cross-view face recognition. Specially, we introduce Convolutional Deep Belief Networks (CDBN) as the feature learning model, and an CDBN based interpolating path between the source and target views is built to model the correlation of cross-view data. The promising results outperform other state-of-the-art methods.
ER -
English
