摘要
基于稀疏表示的虹膜识别方法(SRIR)相对于传统的虹膜识别方法,在处理噪声干扰等问题,识别效果相对较好,具有较好的鲁棒性。但在样本集不足的情况下,识别性能受到影响,存在运行耗时过多、计算复杂度较高的问题。针对上述问题,提出了一种联合多尺度分块和协作表示的虹膜识别算法。通过将虹膜图像按照多个尺度大小分别进行均匀分块,从而达到有效地利用虹膜特征,然后分别对每个尺度下的虹膜图像子块进行基于协作表示的识别,以降低算法耗时,最后将识别结果通过贝叶斯融合方法得到最终的分类。实验结果表明,该算法对于虹膜样本集较少的问题,比原有的SRIR方法耗时低,识别率高,复杂度低。
Iris recognition based sparse recognition （SRIR） is very competitive with traditional recognition approaches on effectiveness and robustness. However, the recognition rate will drop dramatically when the available training samples per subject are very limited, and the computational cost is high. To solve this problem, iris recognition is operating collaborative representation on multi-scale patches and combining the recognition outputs of all patches. Instead of recognition the entire iris image directly, the iris image is divided into several non-overlapping patches with the same scale. Considering the fact that patches on different scales could have complementary information for classification, iris images are patched on multi-scale. The different multi-scale patches are recognized separately based collaborative representation which reduces the computational complexity, while the ensemble of multi-scale outputs is achieved by Bayesian fusion. Experimental results on iris databases show that, although both training and testing image per subject might be very limited, the proposed method outperforms the state-of-the-art recognition approaches on effectiveness and computational cost.
出处
《信号处理》
CSCD
北大核心
2014年第9期1025-1033,共9页
Journal of Signal Processing
基金
国家自然科学基金(61071200)资助课题
河北省自然科学基金(F2014203076)资助课题
关键词
虹膜识别
稀疏表示
协作表示
贝叶斯融合
iris recognition
sparse representation
collaborative representation
Bayesian fusion
作者简介
陈书贞女，1968年生，河北定州人，硕士，燕山大学信息科学与工程学院副教授，主要从事图像处理、模式识别及压缩传感等领域的研究。E—mail：then_sz818@163．com
于倩女，1986年生，河北邢台人，燕山大学硕士研究生，主要研究方向为模式识别和图像处理技术。E—mail：18733448126@163．com
练秋生男，1969年生，江西遂川人，博士，燕山大学信息科学与工程学院教授，主要从事图像处理、压缩传感及稀疏表示等领域的研究。E—mail：lianqs@ysu．edu．cn
