Title: Machine learning-based iris liveness detection using fusion of Thepade SBTC and Niblack binarisation technique

Authors: Sudeep D. Thepade; Bhumika Patil; Smita Khade

Addresses: Computer Engineering Department, Pimpri Chinchwad College of Engineering, Pune, India ' Computer Engineering Department, Pimpri Chinchwad College of Engineering, Pune, India ' Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India

Abstract: Liveness authentication is crucial in the observation environment, especially at border crossings and locations with a combat or buffer zone. It is determined in this study how to assess the liveness of the iris template to avoid fraud. This study uses a handcrafted method called TSBTC and additional binarisation techniques to survey the IIIT Delhi and Clarkson datasets and improve accuracy. A current requirement is to acquire an ILD dataset that covers all typical iris spoofing attempts. Three classifications of eyes are included in the dataset: normal, coloured, and transparent. On every image TSBTC, TSBTC + Niblack binarisation is applied, and further comparison is done on the based-on accuracies. Different classifiers are used for comparison, and Weka software has been used to compare the accuracies of the classifiers used. The study has investigated the method for extracting the local and global features from iris images.

Keywords: iris liveness detection; ILD; Biometrics; Niblack binarisation; machine learning; feature fusion; Thepade SBTC; security.

DOI: 10.1504/IJCVR.2025.147488

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.4, pp.531 - 544

Received: 17 Mar 2023
Accepted: 15 Nov 2023
Published online: 18 Jul 2025 *

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