Ear recognition using local binary patterns: A comparative experimental study

作     者：Hassaballah, M. Alshazly, Hammam A. Ali, Abdelmgeid A. 

作者机构：South Valley Univ Comp Sci Dept Fac Comp & Informat Luxor Egypt South Valley Univ Math Dept Fac Sci Qena 83523 Egypt Minia Univ Comp Sci Dept Fac Comp & Informat Al Minya 61519 Egypt 

出 版 物：《EXPERT SYSTEMS WITH APPLICATIONS》 (专家系统及其应用)

年 卷 期：2019年第118卷

页      面：182-200页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors would like to thank the Editor-in-Chief Prof. Binshan Lin and the anonymous editor and reviewers for their valuable comments and constructive suggestions  which considerably improved the quality of the paper. Also  the owners of all ear datasets that are used in the experiments in this study are gratefully acknowledged 

主　　题：Intelligent biometrics systems Identity verification Ear recognition Local binary patterns Experimental evaluation 

摘      要：Identity recognition using local features extracted from ear images has recently attracted a great deal of attention in the intelligent biometric systems community. The rich and reliable information of the human ear and its stable structure over a long period of time present ear recognition technology as an appealing choice for identifying individuals and verifying their identities. This paper considers the ear recognition problem using local binary patterns (LBP) features. Where, the LBP-like features characterize the spatial structure of the image texture based on the assumption that this texture has a pattern and its strength (amplitude)-two locally complementary aspects. Their high discriminative power, invariance to monotonic gray-scale changes and computational efficiency properties make the LBP-like features suitable for the ear recognition problem. Thus, the performance of several recent LBP variants introduced in the literature as feature extraction techniques is investigated to determine how can they be best utilized for ear recognition. To this end, we carry out a comprehensive comparative study on the identification and verification scenarios separately. Besides, a new variant of the traditional LBP operator named averaged local binary patterns (ALBP) is proposed and its ability in representing texture of ear images is compared with the other LBP variants. The ear identification and verification experiments are extensively conducted on five publicly available constrained and unconstrained benchmark ear datasets stressing various imaging conditions;namely IIT Delhi (I), IIT Delhi (II), AMI, WPUT and AWE. The obtained results for both identification and verification indicate that the current LBP texture descriptors are successful feature extraction candidates for ear recognition systems in the case of constrained imaging conditions and can achieve recognition rates reaching up to 99%;while, their performance faces difficulties when the level of distortions