Abstract
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.
Original language | English |
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Title of host publication | Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 471-476 |
Number of pages | 6 |
ISBN (Electronic) | 9781538633540 |
DOIs | |
Publication status | Published - 2018 Dec 13 |
Event | 4th Asian Conference on Pattern Recognition, ACPR 2017 - Nanjing, China Duration: 2017 Nov 26 → 2017 Nov 29 |
Other
Other | 4th Asian Conference on Pattern Recognition, ACPR 2017 |
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Country | China |
City | Nanjing |
Period | 17/11/26 → 17/11/29 |
Keywords
- Deep Learning
- Face Recognition
- Siamese network
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Signal Processing