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

Sensors

Jun Cai²  Jing Chen¹ 

[1] School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China;

关键词: intra-class variation model differences;    face recognition;    sparse representation;   

DOI  :  10.3390/s150101071

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.