Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

Implementation of Face Recognition Based on Deep Learning Framework Caffe

Authors
Zijiang Zhu, Xiaoguang Deng, Yi Hu, Dong Liu, Junshan Li
Corresponding Author
Zijiang Zhu
Available Online March 2018.
DOI
10.2991/mecae-18.2018.144How to use a DOI?
Keywords
Deep learning, Neural network, Caffe, Face recognition.
Abstract

At present, deep learning has been recognized as the best research direction of face recognition. This paper describes the basic principle of neural network technology, uses deep learning framework Caffe to build neural network, and realizes face recognition by experiment. It also puts forward the major issues for face recognition research by the use of neural network, which provides reference for construction and implementation of Caffe Face Recognition.

Copyright
© 2018, 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 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
Series
Advances in Engineering Research
Publication Date
March 2018
ISBN
10.2991/mecae-18.2018.144
ISSN
2352-5401
DOI
10.2991/mecae-18.2018.144How to use a DOI?
Copyright
© 2018, 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  - Zijiang Zhu
AU  - Xiaoguang Deng
AU  - Yi Hu
AU  - Dong Liu
AU  - Junshan Li
PY  - 2018/03
DA  - 2018/03
TI  - Implementation of Face Recognition Based on Deep Learning Framework Caffe
BT  - Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
SP  - 189
EP  - 194
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-18.2018.144
DO  - 10.2991/mecae-18.2018.144
ID  - Zhu2018/03
ER  -