Patch based Semi-supervised Linear Regression for Face Recognition

• Yuhua Ding,
• Fan Liu,
• Ting Rui,
• Zhenmin Tang,

Vol. 13, No. 8, August 30, 2019
10.3837/tiis.2019.08.008, Download Paper (Free):

• Face Recognition
• semi-supervised
• single sample per person
• linear regression

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 ••• . The solutions of all the mapping matrices are integrated into an overall objective function, which uses l_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.

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