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http://dx.doi.org/10.3837/tiis.2019.08.008

Patch based Semi-supervised Linear Regression for Face Recognition  

Ding, Yuhua (School of Computer Science and Engineering, Nanjing University of Science and Technology)
Liu, Fan (College of Computer and Information, Hohai University)
Rui, Ting (Engineering Institute of Engineer Crops, PLA University of Science and Technology)
Tang, Zhenmin (School of Computer Science and Engineering, Nanjing University of Science and Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.8, 2019 , pp. 3962-3980 More about this Journal
Abstract
To deal with single sample face recognition, this paper presents a patch based semi-supervised linear regression (PSLR) algorithm, which draws facial variation information from unlabeled samples. Each facial image is divided into overlapped patches, and a regression model with mapping matrix will be constructed on each patch. Then, we adjust these matrices by mapping unlabeled patches to $[1,1,{\cdots},1]^T$. The solutions of all the mapping matrices are integrated into an overall objective function, which uses ${\ell}_{2,1}$-norm minimization constraints to improve discrimination ability of mapping matrices and reduce the impact of noise. After mapping matrices are computed, we adopt majority-voting strategy to classify the probe samples. To further learn the discrimination information between probe samples and obtain more robust mapping matrices, we also propose a multistage PSLR (MPSLR) algorithm, which iteratively updates the training dataset by adding those reliably labeled probe samples into it. The effectiveness of our approaches is evaluated using three public facial databases. Experimental results prove that our approaches are robust to illumination, expression and occlusion.
Keywords
face recognition; semi-supervised; single sample per person; linear regression;
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