Human Identification Through Iris Recognition Based on Genetic Algorithm and Machine Learning

The unique structure of the iris has established this biometric trait as an effective method for developing robust and reliable identification systems. However, it is crucial to extract features that provide a thorough description of the individual's iris, as identity recognition is a significant security concern that many businesses must implement correctly. In this study, a combined method is developed for iris segmentation, and a genetic algorithm-based approach is presented to compute optimal features in terms of separability. This approach encompasses three tasks: feature selection, feature weighting through a genetic algorithm, and learning new features through feature combination. The goal of this combined method is to extract features related to iris morphology and texture. Thus, three feature extraction methods, including local binary patterns and Gabor filters, were applied. Subsequently, the weighted genetic algorithm is employed to minimize the dimensions of the features while improving their discrimination ability. In the final detection stage, a single classification algorithm, the support vector machine, is used to implement lightweight classification, facilitating the method's implementation on devices with hardware limitations. Numerical evaluations of this classification demonstrate its acceptable accuracy compared to neural network-based methods. Experiments conducted on two datasets, IITD and CASIA Interval, resulted in detection rates of 99.55% and 93.50%, respectively. In comparison with state-of-the-art approaches, there is a meaningful difference in the outcomes of the proposed method.