Robust Face Recognition via Double Low-Rank Matrix Recovery for Feature Extraction

Ming Yin, Shuting Cai, Junbin Gao

Research output: Book chapter/Published conference paperConference paper

23 Citations (Scopus)
4 Downloads (Pure)


Feature extraction is one of the most fundamental problems in face recognition tasks. In this paper, motivated by low rank representation (LRR) model on exploring the multiple subspace structures of observation data, we propose a double low-rank matrix recovery method to learn low-rank subspaces from face images, where it takes into account the recovery of row space and column space information simultaneously. Applying Augmented Lagrangian Multiplier (ALM), the optimization problem on minimization of nuclear norm is resolve defficiently. By evaluating on public face databases,experimental results show that our proposed method works much better than existing face recognition methods based on feature extraction. It is more robust to outliers, varying illumination and occlusion.
Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781479923410
Publication statusPublished - 2013
Event2013 20th IEEE International Conference on Image Processing: ICIP 2013 - Melbourne Convention and Exhibition Centre, Melbourne, Australia
Duration: 15 Sep 201318 Sep 2013


Conference2013 20th IEEE International Conference on Image Processing
OtherThe International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP 2013, the twentieth in the series that has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world.
Internet address

Grant Number

  • DP130100364

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