Review on secure traditional and machine learning algorithms for age prediction using IRIS image

Iris recognition is a secure and best-chosen biometric application in the digital world because of its unique characteristics. Day by day, the digital world plays a significant role in human life for various applications. The applications are vastly spread over secure applications of the nation such as border control applications, criminal investigations, postmortem studies, access the digital equipment, smart homes, smart appliances, smart cars etc. Due to the digitalization of the world, all the research communities, scientists, and industries are focusing on the biometric-based secured iris recognition system. Several researchers have done much work in this domain, but there is still a scope of improvement for various reasons, i.e., less speed and accuracy of the module. The researcher has implemented various algorithms based on traditional and neural network architectures. In this scenario, this paper gives a brief on different techniques and algorithms used by researchers to predict the age of human people using the iris. This paper discussed one hundred and one papers in the literature with various image segmentation, feature extraction and classification of the iris. This paper summarizes publicly available standard databases and various evaluation parameters, i.e., accuracy, precision, recall, f-score, etc. The research community evaluated the age prediction through the iris-based state-of-the-art algorithms with secure prediction, i.e., TPR, TNR, FPR, FNR. Finally, this paper provides the strengths and weaknesses of the various state of art algorithms, respectively, and summarizes the gaps in the existing technology with the scope of improvement.