In biometrics, periocular recognition analysis is an essential constituent for identifying the human being. Among prevailing the modalities, ocular biometric traits such as iris, sclera and periocular eye movement have experienced noteworthy consciousness in the recent past. In this paper, we are presenting new multi-biometric fusion method called Phase Intensive Mutual Exclusive Distribution (PI-MED) method by combining periocular features (i.e. iris and sclera) for identity verification. The main objective of the proposed PI-MED method is to reduce the matching fusion time and overhead during human recognition in biometrics. Initially, iris modality and sclera modality is pre-processed using Phase Intensive Rubber Sheeting Local Pattern Extraction to generate the vector of score. After that, the extracted iris and sclera features are given to the Mutual Exclusive Bayesian fusion model. The fusion model is applied at the score level for reducing fusion overhead. In this model, feature fusion is generated based on the log likelihood ratio by using covariance matrix measurement. Finally with fusion features, Distributed Hamming Distance Template Matching (DHDTM) algorithm is designed to evaluate the recognition rate of test data with available training data. The results show that the DHDTM significantly improves the recognition rate of human biometric samples when compared to the conventional person identification methods. Several tests were conducted to evaluate the performance of the proposed methods of standard biometric databases using three metrics, namely, matching fusion time, overhead and true positive rate. From the experimental results, the proposed PI-MED method reduces the matching fusion time and overhead by 47% and 45% when compared to existing methods. Similarly, the proposed PI-MED method increases the true positive rate by 33% when compared to existing methods.

在生物识别技术中，眼周识别分析是识别人的重要组成部分。在主要的方式中，诸如虹膜，巩膜和眼周眼运动之类的眼部生物特征最近已引起人们的注意。在本文中，我们将结合眼周特征（即虹膜和巩膜），提出一种新的多生物融合方法​​，称为相密集互斥分布（PI-MED）方法，以进行身份​​验证。提出的PI-MED方法的主要目的是减少生物识别中人类识别期间的匹配融合时间和开销。最初，虹膜模态和巩膜模态使用相密集橡胶片局部模式提取进行预处理，以生成分数矢量。之后，提取的虹膜和巩膜特征被提供给互斥贝叶斯融合模型。在分数级别应用融合模型以减少融合开销。在该模型中，通过使用协方差矩阵测量基于对数似然比生成特征融合。最后，通过融合功能，设计了分布式汉明距离模板匹配（DHDTM）算法，以评估具有可用训练数据的测试数据的识别率。结果表明，与传统的人员识别方法相比，DHDTM显着提高了人体生物特征样本的识别率。进行了一些测试，以使用三种度量标准（即匹配融合时间，开销和真实阳性率）评估标准生物特征数据库建议方法的性能。从实验结果来看，与现有方法相比，所提出的PI-MED方法将匹配融合时间和开销减少了47％和45％。同样，与现有方法相比，提出的PI-MED方法将真实阳性率提高了33％。