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VOL. 9, ISSUE 2 (2024)
A systematic literature review and analysis of Iris recognition performance using efficient machine learning techniques
Authors
Jyoti Prakash Singh, Dr. Vikas Sakalle
Abstract
Iris recognition has emerged as one of the most reliable biometric
authentication methods, offering high accuracy and non-invasiveness for
security applications. This systematic literature review synthesizes recent
advances in iris recognition systems utilizing efficient machine learning
techniques published between 2020 and 2025. Through comprehensive analysis of
30+ peer-reviewed papers, we evaluate recognition accuracy, feature extraction
methods, dataset benchmarks, and computational efficiency across multiple
machine learning architectures. State-of-the-art techniques including
convolutional neural networks (CNNs), Vision Transformers, generative
adversarial networks (GANs), and lightweight models demonstrate recognition
accuracy ranging from 85% to 100% depending on imaging conditions and dataset
complexity. Our findings reveal that while deep learning models achieve
superior accuracy (93-99%), lightweight architectures like MobileNetV3 and
condensed CNNs achieve competitive performance (95-98%) with over 1000× fewer
parameters, making them suitable for mobile and edge-device deployment. The
review identifies critical research gaps including standardized evaluation
protocols, privacy-preserving template protection, and cross-spectrum
heterogeneous matching capabilities. We conclude that the future of iris
recognition lies in balancing accuracy with computational efficiency while
addressing real-world challenges such as non-ideal imaging conditions,
presentation attack detection, and multimodal biometric integration. This
review provides practitioners, researchers, and institutions with comprehensive
guidance on technique selection, dataset utilization, and implementation
strategies for iris recognition systems.
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Pages:19-29
How to cite this article:
Jyoti Prakash Singh, Dr. Vikas Sakalle "A systematic literature review and analysis of Iris recognition performance using efficient machine learning techniques". International Journal of Advanced Science and Research, Vol 9, Issue 2, 2024, Pages 19-29
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