KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Generic Training Set based Multimanifold Discriminant Learning for Single Sample Face Recognition

  • Dong, Xiwei (College of Automation, Nanjing University of Posts and Telecommunications) ;
  • Wu, Fei (College of Automation, Nanjing University of Posts and Telecommunications) ;
  • Jing, Xiao-Yuan (College of Automation, Nanjing University of Posts and Telecommunications)
  • Received : 2016.04.11
  • Accepted : 2017.09.05
  • Published : 2018.01.31
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Abstract

Face recognition (FR) with a single sample per person (SSPP) is common in real-world face recognition applications. In this scenario, it is hard to predict intra-class variations of query samples by gallery samples due to the lack of sufficient training samples. Inspired by the fact that similar faces have similar intra-class variations, we propose a virtual sample generating algorithm called k nearest neighbors based virtual sample generating (kNNVSG) to enrich intra-class variation information for training samples. Furthermore, in order to use the intra-class variation information of the virtual samples generated by kNNVSG algorithm, we propose image set based multimanifold discriminant learning (ISMMDL) algorithm. For ISMMDL algorithm, it learns a projection matrix for each manifold modeled by the local patches of the images of each class, which aims to minimize the margins of intra-manifold and maximize the margins of inter-manifold simultaneously in low-dimensional feature space. Finally, by comprehensively using kNNVSG and ISMMDL algorithms, we propose k nearest neighbor virtual image set based multimanifold discriminant learning (kNNMMDL) approach for single sample face recognition (SSFR) tasks. Experimental results on AR, Multi-PIE and LFW face datasets demonstrate that our approach has promising abilities for SSFR with expression, illumination and disguise variations.

Keywords

Acknowledgement

Supported by : National Science Foundation of China (NSFC)

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