Proceedings of the 2016 International Conference on Computer Engineering and Information Systems

Robust Face Recognition Based on DCNN and CRC

Authors
Li-Na Yuan, Feng Cen
Corresponding Author
Li-Na Yuan
Available Online November 2016.
DOI
10.2991/ceis-16.2016.23How to use a DOI?
Keywords
face recognition;sparse representation; collabora-tive representation;deep learning
Abstract

Collaborative representation based classification (CRC) has gained popularity in recent years, but the conventional features could not effectively handle the variations of pose and occlusion such as sunglass. In this paper, the deep convolution neural network (DCNN) is introduced in the feature extraction to tackle the facial deformation, particularly, such as pose variation. After analysis on the feasibility of the dictionary construction on DCNN based features, a DCNN feature based occlusion dictionary computing algorithm is then presented to tackle the face recognition with occlusion. Experiments on representative face databases with variations of pose and occlusion demonstrated the effectiveness of the proposed algorithm scheme.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 International Conference on Computer Engineering and Information Systems
Series
Advances in Computer Science Research
Publication Date
November 2016
ISBN
10.2991/ceis-16.2016.23
ISSN
2352-538X
DOI
10.2991/ceis-16.2016.23How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Li-Na Yuan
AU  - Feng Cen
PY  - 2016/11
DA  - 2016/11
TI  - Robust Face Recognition Based on DCNN and CRC
BT  - Proceedings of the 2016 International Conference on Computer Engineering and Information Systems
PB  - Atlantis Press
SP  - 117
EP  - 122
SN  - 2352-538X
UR  - https://doi.org/10.2991/ceis-16.2016.23
DO  - 10.2991/ceis-16.2016.23
ID  - Yuan2016/11
ER  -