Deep face recognition under eyeglass and scale variation using extended siamese network

Fan Qiu, Seiichiro Kamata, Lizhuang Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages471-476
Number of pages6
ISBN (Electronic)9781538633540
DOIs
Publication statusPublished - 2018 Dec 13
Event4th Asian Conference on Pattern Recognition, ACPR 2017 - Nanjing, China
Duration: 2017 Nov 262017 Nov 29

Other

Other4th Asian Conference on Pattern Recognition, ACPR 2017
CountryChina
CityNanjing
Period17/11/2617/11/29

Keywords

  • Deep Learning
  • Face Recognition
  • Siamese network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Qiu, F., Kamata, S., & Ma, L. (2018). Deep face recognition under eyeglass and scale variation using extended siamese network. In Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017 (pp. 471-476). [8575869] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACPR.2017.48