TY - GEN
T1 - Deep face recognition under eyeglass and scale variation using extended siamese network
AU - Qiu, Fan
AU - Kamata, Sei Ichiro
AU - Ma, Lizhuang
N1 - Funding Information:
This work was partially supported by JSPS KAK-ENHI Grant Number 15K00248 and fund of Shanghai Science and Technology Commission Grant Number 16511101300.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/12/13
Y1 - 2018/12/13
N2 - Face recognition has attracted much attention from researchers for past decades. Recently, with the development of deep learning, a deep neural network is adopted by face recognition system and better performance is obtained. Many works on metric learning have been done in the deep neural network. Meanwhile, there are several variation problems existing in face recognition, such as profile face image, low-resolution face image, different age of face image, face image wearing eyeglass, etc. In this paper, targeting at different kinds of variation problems, we proposed a novel network structure, called Extended Siamese Network. Another contribution is that a new loss function is proposed, to further take inter-class information into account based on the center loss function. The experiments show that recognition accuracy is improved in comparison with the other state-of-Art methods.
AB - Face recognition has attracted much attention from researchers for past decades. Recently, with the development of deep learning, a deep neural network is adopted by face recognition system and better performance is obtained. Many works on metric learning have been done in the deep neural network. Meanwhile, there are several variation problems existing in face recognition, such as profile face image, low-resolution face image, different age of face image, face image wearing eyeglass, etc. In this paper, targeting at different kinds of variation problems, we proposed a novel network structure, called Extended Siamese Network. Another contribution is that a new loss function is proposed, to further take inter-class information into account based on the center loss function. The experiments show that recognition accuracy is improved in comparison with the other state-of-Art methods.
KW - Deep Learning
KW - Face Recognition
KW - Siamese network
UR - http://www.scopus.com/inward/record.url?scp=85060511792&partnerID=8YFLogxK
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U2 - 10.1109/ACPR.2017.48
DO - 10.1109/ACPR.2017.48
M3 - Conference contribution
AN - SCOPUS:85060511792
T3 - Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
SP - 471
EP - 476
BT - Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th Asian Conference on Pattern Recognition, ACPR 2017
Y2 - 26 November 2017 through 29 November 2017
ER -