A jointly local structured sparse deep learning network for face recognition

Renjie Wu, Seiichiro Kamata

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

1 Citation (Scopus)

Abstract

In this paper, we proposed an optimized Sparse Deep Learning Network (SDLN) model for Face Recognition (FR). A key contribution of this work is to learn feature coding of human face with a SDLN based on local structured Sparse Representation (SR). In traditional sparse FR methods, different poses and expressions of training samples could have great influence on the recognition results. We consider the SR that should be guided by context constraints which are defined by the correlations of dictionary atoms. The over-complete common dictionary that contains common atom set has been learned from a local region structured sparse encoding process. We obtained over-complete common dictionary and feature coding for each face. As we all know that the deep learning has been widely applied to face feature learning. Using traditional deep learning methods can not contain variations of face identity information. We have to get face features of compatible change in a jointly deep learning network. The proposed SDLN is jointly fine-tuned to optimize for the task of FR. The SDLN achieves high FR performance on the ORL and FERET database.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages3026-3030
Number of pages5
Volume2016-August
ISBN (Electronic)9781467399616
DOIs
Publication statusPublished - 2016 Aug 3
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: 2016 Sep 252016 Sep 28

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
CountryUnited States
CityPhoenix
Period16/9/2516/9/28

Fingerprint

Face recognition
Glossaries
Atoms
Deep learning

Keywords

  • Atom decomposition
  • Face recognition
  • Over-complete dictionary learning
  • Restricted boltzmann machine
  • Sparse deep learning network

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Wu, R., & Kamata, S. (2016). A jointly local structured sparse deep learning network for face recognition. In 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings (Vol. 2016-August, pp. 3026-3030). [7532915] IEEE Computer Society. https://doi.org/10.1109/ICIP.2016.7532915

A jointly local structured sparse deep learning network for face recognition. / Wu, Renjie; Kamata, Seiichiro.

2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. Vol. 2016-August IEEE Computer Society, 2016. p. 3026-3030 7532915.

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

Wu, R & Kamata, S 2016, A jointly local structured sparse deep learning network for face recognition. in 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. vol. 2016-August, 7532915, IEEE Computer Society, pp. 3026-3030, 23rd IEEE International Conference on Image Processing, ICIP 2016, Phoenix, United States, 16/9/25. https://doi.org/10.1109/ICIP.2016.7532915
Wu R, Kamata S. A jointly local structured sparse deep learning network for face recognition. In 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. Vol. 2016-August. IEEE Computer Society. 2016. p. 3026-3030. 7532915 https://doi.org/10.1109/ICIP.2016.7532915
Wu, Renjie ; Kamata, Seiichiro. / A jointly local structured sparse deep learning network for face recognition. 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. Vol. 2016-August IEEE Computer Society, 2016. pp. 3026-3030
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