Performance Analysis of Variants of Naïve Bayes Classifier to Identify Classes of Flowers Using Iris Recognition System

Iris recognition is a biometric authentication technique that utilizes distinctive iris features to verify and identify individuals. It is not related to the identification of variants of species of flowers. The utilization of iris texture presents several benefits such as sustained stability, user-friendliness, and exceptional precision in identification. The extraction of effective features is considered the most crucial stage in the iris recognition system. A significant proportion of preceding techniques for iris recognition feature extraction relied on manually engineered features. The present study aims to explicate the process of constructing a proficient iris recognition system, which involves several phases of design. The process entails comprehending and delineating the issue, preparing and scrutinizing the data, implementing three iterations of the Naive Bayes algorithm, specifically Gaussian, Bernoulli, and Multinomial, and subsequently forecasting the precision of the system.