A Comprehensive Analysis of Gender Identification from Iris Images using Artificial Intelligence Technique

Gender identification using iris images is the process of identifying an individual's gender by analyzing the patterns in their eyes. However, iris images are sometimes blurry, leading to misclassification. To overcome the problem, various existing studies have explored gender identification from iris images using Artificial Intelligence (AI) techniques. This paper analyses gender identification using iris image AI techniques such as Machine Learning (ML) and Deep Learning (DL) are utilized to categorize gender like male and female using iris images. The current researches employ ML techniques such as Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and Principal Component Analysis (PCA). Additionally, DL technique such as Deep Neural Network (DNN), Convolutional Neural Network (CNN), and pretrained CNN models like AlexNet, VGG16, VGG19, ResNet10, ResNet50, DenseNet20, and InceptionV3 have also been utilized for gender identification using iris images. To evaluate the performance of these existing models, the evaluation metrics of accuracy, precision, recall, and F1-score have been taken into consideration.