A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 5 Issue 2
Mar.  2018

IEEE/CAA Journal of Automatica Sinica

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    CiteScore: 11.2, Top 5% (Q1)
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Article Contents
Jianquan Gu, Haifeng Hu and Haoxi Li, "Local Robust Sparse Representation for Face Recognition With Single Sample per Person," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 547-554, Mar. 2018. doi: 10.1109/JAS.2017.7510658
Citation: Jianquan Gu, Haifeng Hu and Haoxi Li, "Local Robust Sparse Representation for Face Recognition With Single Sample per Person," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 547-554, Mar. 2018. doi: 10.1109/JAS.2017.7510658

Local Robust Sparse Representation for Face Recognition With Single Sample per Person

doi: 10.1109/JAS.2017.7510658
Funds:

the National Natural Science Foundation of China 61673402

the National Natural Science Foundation of China 61273270

the National Natural Science Foundation of China 60802069

the Natural Science Foundation of Guangdong Province 2017A030311029

the Natural Science Foundation of Guangdong Province 2016B010109002

the Natural Science Foundation of Guangdong Province 2015B090912001

the Natural Science Foundation of Guangdong Province 2016B010123005

the Natural Science Foundation of Guangdong Province 2017B090909005

the Science and Technology Program of Guangzhou of China 201704020180

the Science and Technology Program of Guangzhou of China 201604020024

More Information
  • The purpose of this paper is to solve the problem of robust face recognition (FR) with single sample per person (SSPP). In the scenario of FR with SSPP, we present a novel model local robust sparse representation (LRSR) to tackle the problem of query images with various intra-class variations, e.g., expressions, illuminations, and occlusion. FR with SSPP is a very difficult challenge due to lacking of information to predict the possible intra-class variation of the query images. The key idea of the proposed method is to combine a local sparse representation model and a patch-based generic variation dictionary learning model to predict the possible facial intra-class variation of the query images. The experimental results on the AR database, Extended Yale B database, CMU-PIE database and LFW database show that the proposed method is robust to intra-class variations in FR with SSPP, and outperforms the state-of-art approaches.

     

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