Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers  

作　　者：Smita Khade Shilpa Gite Sudeep D.Thepade Biswajeet Pradhan Abdullah Alamri 

机构地区：[1]Symbiosis International(Deemed University),Symbiosis Institute of Technology,Pune,412115,India [2]Symbiosis Centre for Applied Artificial Intelligence,Symbiosis International(Deemed University),Pune,412115,India [3]Computer Engineering,Pimpri Chinchwad College of Engineering,Pune,411044,India [4]Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),School for Civil and Environmental Engineering,Faculty of Engineering and Information Technology,University of Technology Sydney,Sydney,New SouthWales,2007,Australia [5]Earth Observation Center,Institute of Climate Change,University Kebangsaan Malaysia,UKM,Bangi,Selangor,43600,Malaysia [6]Department of Geology&Geophysics,College of Science,King Saud University,P.O.Box 2455,Riyadh,11451,Saudi Arabia

出　　处：《Computer Modeling in Engineering & Sciences》2023年第7期323-345,共23页工程与科学中的计算机建模（英文）

基　　金：supported by theResearchers Supporting Project No.RSP-2021/14,King Saud University,Riyadh,Saudi Arabia.

摘　　要：Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from spreading.Biometric systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses,replayed the video,and print attacks.The work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris liveness.Seven assorted feature creation ways are studied in the presented solutions,and these created features are explored for the training of eight distinct machine learning classifiers and ensembles.The predicted iris liveness identification variants are evaluated using recall,F-measure,precision,accuracy,APCER,BPCER,and ACER.Three standard datasets were used in the investigation.The main contribution of our study is achieving a good accuracy of 99.18%with a smaller feature vector.The fragmental coefficients of Haar transformed iris image of size 8∗8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size(64 features).Random forest gave 99.18%accuracy.Additionally,conduct an extensive experiment on cross datasets for detailed analysis.The results of our experiments showthat the iris biometric template is decreased in size tomake the proposed framework suitable for algorithmic verification in real-time environments and settings.

关 键 词：Iris images liveness identification Haar transform machine learning BIOMETRIC feature formation ensemble model 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]