Comparative Analysis of Machine Learning Algorithms for Biometric Iris Recognition Systems

Abstract: Background: Biometric identification plays a crucial role in various industries such as retail, and banking. Among the different biometric traits, iris patterns have become a reliable means of identification due to their unique features. In our thesis, we focus on evaluating and comparing different machine learning algorithms for irisrecognition. The main aim is to identify the algorithm that achieves the highestperformance for iris recognition. Objectives: The main objective of the thesis is to train, test, and evaluate the best performing model using the iris image dataset among the selected algorithmsthrough a literature review. Additionally, the goal is to compare different algorithms for a biometric recognition system that relies on iris features. Methods: Our research is supported by an extensive literature review that usesa wide range of scholarly articles specifically focused on iris recognition. Experimentation is also used to determine the most accurate machine-learning algorithm interms of accuracy. Results: Our experimentation results revealed that the accuracy rates for all themodels were as follows: CNN obtained the highest accuracy at 98.7%, while SVM and the SVM combination with hamming distance achieved 86% and 80%, respectively. Based on our research findings, we conclude that including hamming distancewith SVM did not result in improved accuracy compared to other classification algorithms. Finally, CNN achieved high accuracy in comparison to different algorithmsfor iris recognition. Conclusions: To achieve our research goals, we divided the dataset into three parts: 60% for training, 20% for testing, and another 20% for validation. Different techniques were used to train the algorithm with the training dataset. The results aretested for every algorithm to determine its accuracy. Among the selected algorithms, the convolutional neural network delivered an accurate performance with an accuracy of 98.7%. By employing performance metrics, we have effectively addressed theresearch questions and identified the most accurate algorithm for the iris recognitionsystem.