IMPROVING IRIS RECOGNITION ACCURACY WITH MACHINE LEARNING METHODS

Authors

  • Rajitha.K, Banothu Mounika, Pesaru Anvith Reddy

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This paper presents a comprehensive study on iris recognition utilizing advanced machine learning techniques to enhance biometric authentication systems.

Abstract

This paper presents a comprehensive study on iris recognition utilizing advanced machine learning techniques to enhance biometric authentication systems. Iris recognition has gained prominence as a reliable method for personal identification due to the unique and stable patterns found in human irises. This research explores various machine learning algorithms, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and k-Nearest Neighbors (k-NN), to evaluate their effectiveness in accurately classifying iris patterns. We employ a diverse dataset of iris images, focusing on feature extraction and selection methods that maximize classification accuracy while minimizing computational complexity. Experimental results demonstrate that our proposed approach significantly outperforms traditional methods, achieving high accuracy rates and robust performance across varying conditions. Additionally, the paper discusses the challenges associated with iris recognition, such as image quality and environmental factors, while offering insights into future directions for improving system reliability and scalability. This research underscores the potential of machine learning techniques in advancing iris recognition technology, ultimately contributing to the field of secure biometric identification.

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