Open Access

ARTICLE

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

by Smita Khade¹, Shilpa Gite^1,2,*, Sudeep D. Thepade³, Biswajeet Pradhan^4,5,*, Abdullah Alamri⁶

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 South Wales, 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

* Corresponding Authors: Shilpa Gite. Email: ; Biswajeet Pradhan. Email:

(This article belongs to the Special Issue: AI-Driven Engineering Applications)

Computer Modeling in Engineering & Sciences 2023, 136(1), 323-345. https://doi.org/10.32604/cmes.2023.023674

Received 09 May 2022; Accepted 13 September 2022; Issue published 05 January 2023

Abstract

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 show that the iris biometric template is decreased in size to make the proposed framework suitable for algorithmic verification in real-time environments and settings.

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