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Sensors 2015, 15(1), 1071-1087; doi:10.3390/s150101071

Single-Sample Face Recognition Based on Intra-Class Differences in a Variation Model

School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China
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Received: 17 September 2014 / Accepted: 10 December 2014 / Published: 8 January 2015
(This article belongs to the Special Issue HCI In Smart Environments)
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Abstract

In this paper, a novel random facial variation modeling system for sparse representation face recognition is presented. Although recently Sparse Representation-Based Classification (SRC) has represented a breakthrough in the field of face recognition due to its good performance and robustness, there is the critical problem that SRC needs sufficiently large training samples to achieve good performance. To address these issues, we challenge the single-sample face recognition problem with intra-class differences of variation in a facial image model based on random projection and sparse representation. In this paper, we present a developed facial variation modeling systems composed only of various facial variations. We further propose a novel facial random noise dictionary learning method that is invariant to different faces. The experiment results on the AR, Yale B, Extended Yale B, MIT and FEI databases validate that our method leads to substantial improvements, particularly in single-sample face recognition problems. View Full-Text
Keywords: intra-class variation model differences; face recognition; sparse representation intra-class variation model differences; face recognition; sparse representation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Cai, J.; Chen, J.; Liang, X. Single-Sample Face Recognition Based on Intra-Class Differences in a Variation Model. Sensors 2015, 15, 1071-1087.

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