Title: Feature extraction methods for iris recognition systems: a comprehensive survey

Authors: Amir Azizi; Panayiotis Charalambous

Addresses: CYENS, Centre of Excellence, Dimarchou Lellou Demetriadi 01, Nicosia 1016, Cyprus ' CYENS, Centre of Excellence, Dimarchou Lellou Demetriadi 01, Nicosia 1016, Cyprus

Abstract: In iris recognition, optimal feature extraction and subset selection are critical for achieving high-accuracy identification. While traditional techniques - such as statistical analysis, frequency-domain transformations, and texture descriptors - have been extensively employed, the past decade has witnessed the rise of deep learning as the dominant paradigm, reshaping the field. This paper presents a comprehensive survey of recent developments, with a particular emphasis on deep learning-based methods for feature extraction and selection. Over 250 sources, including scholarly articles, benchmark datasets, and institutional resources, are reviewed. The paper begins with an overview of classical approaches, followed by an in-depth examination of various deep learning architectures. Each method is analysed in terms of its core principles, innovations, and limitations. Key challenges are identified, and emerging trends are highlighted to guide future research and practical implementations in iris-based biometric systems.

Keywords: biometric; deep learning; iris recognition; image processing.

DOI: 10.1504/IJBM.2026.156246

International Journal of Biometrics, 2026 Vol.18 No.5, pp.447 - 483

Received: 17 Apr 2024
Accepted: 15 Jul 2025
Published online: 08 Sep 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article