Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

作     者：Raniyah Wazirali Rami Ahmed 

作者机构：College of Computing and InformaticsSaudi Electronic UniversityRiyadh11673Saudi Arabia The School of Information TechnologySebha UniversitySebha71Libya 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2022年第41卷第4期

页      面：305-320页

核心收录：

学科分类：0710[理学-生物学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Person identification convolutional neural network local binary pattern periocular region Covid-19 

摘      要：The global pandemic of novel coronavirus that started in 2019 has ser-iously affected daily lives and placed everyone in a panic *** coronavirus led to the adoption of social distancing and people avoiding unneces-sary physical contact with each *** present situation advocates the require-ment of a contactless biometric system that could be used in future authentication systems which makesfingerprint-based person identification ***-lar biometric is the solution because it does not require physical contact and is able to identify people wearing face ***,the periocular biometric region is a small area,and extraction of the required feature is the point of *** paper has proposed adopted multiple features and emphasis on the periocular *** the proposed approach,combination of local binary pattern(LBP),color histogram and features in frequency domain have been used with deep learning algorithms for classifi***,we extract three types of fea-tures for the classification of periocular regions for *** LBP represents the textual features of the iris while the color histogram represents the frequencies of pixel values in the RGB *** order to extract the frequency domain fea-tures,the wavelet transformation is *** learning from these features,a convolutional neural network(CNN)becomes able to discriminate the features and can provide better recognition *** proposed approach achieved the highest accuracy rates with the lowest false person identification.