Infrared and visible face fusion recognition based on extend sparse representation classification and local binary patterns for single sample problem

摘要

While near infrared and visible fusion recognition has been actively researched in recent years, most theoretical results and algorithms concentrate on the sufficient training samples setting. This paper focuses on general fusion method when there are insufficient training samples with one pair of near infrared and visible face image. Compared with existing methods, the proposed method requires neither sufficient samples nor the training step. To get a robust and time-efficient fusion model for unconstrained face recognition with single sample situation, two models are proposed to fuse the local binary patterns based descriptors and sparse representation based classification: the first fusion model fuses directly the representation error, while the second fusion model is an accelerated version with learning a cross-spectral dictionary. Experiments are performed on HITSZ LAB2 database and the experiments results showed that the proposed fusion model extracted the complementary features of near-infrared and visible-light images, the fusion face recognition method had superior performance to state of the art fusion methods.