Modern Voting System Based on Machine Learning-Driven Iris Recognition

In this study, a unique machine learning-based method for iris detection in contemporary voting systems is presented. The suggested technique successfully extracts distinguishing characteristics from iris scans by utilizing Convolutional Neural Networks (CNNs), enabling precise and safe voter identification. The rich patterns and variances found in irises are captured by the CNN architecture, which ensures high classification accuracy. Because of the system's strong performance, there is a far lower chance of fraud and the election process is more honest. With its dependable and expandable voter verification solution, the suggested method advances the creation of safer and more effective voting procedures. The outcomes show how well the CNN-based strategy works to solve the problems with conventional voter identification techniques.