A jointly local structured sparse deep learning network for face recognition

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
出版社IEEE Computer Society
ページ3026-3030
ページ数5
ISBN(電子版)9781467399616
DOI
出版ステータスPublished - 2016 8 3
イベント23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
継続期間: 2016 9 252016 9 28

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
2016-August
ISSN(印刷版)1522-4880

Other

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

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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