Infrared and visible face fusion recognition based on extended sparse representation classification and local binary patterns for the single sample problem
Infrared and visible face fusion recognition based on extended sparse representation classification and local binary patterns for the single sample problem
复制标题
基于扩展稀疏表示分类和局部二值模式的红外与可见光人脸融合识别单样本问题
DOI：
10.1364/jot.86.000408
复制
发表时间：
2019-07
影响因子：
0.4
通讯作者：
Liu G
中科院分区：
文献类型：
--
作者：
Xie Z;Zhang S;Yu X;Liu G
近红外与可见光融合识别是近年来的研究热点，但大多数的理论成果和算法都集中在足够的训练样本设置上。研究了在训练样本不足的情况下，近红外和可见光人脸图像融合的一般方法。与现有的方法相比，该方法既不需要足够的样本，也不需要训练步骤。为了得到一个鲁棒的和时间有效的融合模型的无约束人脸识别在单样本的情况下，提出了两个模型融合的本地二进制模式的描述符和稀疏表示的分类：第一个融合模型直接融合的表示错误，而第二个融合模型是一个加速版本，从交叉光谱字典学习。在HITSZ LAB2数据库上进行了实验，实验结果表明，该融合模型提取了近红外和可见光图像的互补特征。融合人脸识别方法具有上级的性能，最先进的融合方法。
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 the general fusion method when there are insufficient training samples with one pair of near-infrared and visible face images. 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 in the single sample situation, two models are proposed to fuse the local binary pattern based descriptors and the sparse representation based classification: the first fusion model directly fuses the representation error, while the second fusion model is an accelerated version that learns from a cross-spectral dictionary. Experiments are performed on the HITSZ LAB2 database, and the experiment 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.