Iris Recognition System Using Histogram Analysis

Norhuda, Othman (2012) Iris Recognition System Using Histogram Analysis. Project Report. UTeM, Melaka, Malaysia. (Submitted)

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Abstract

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. This report involves the development of iris recognition system using the histogram analysis method the Local Phase Quantization and Rotation Invariant Local Phase Quantization to verify the both the uniqueness of human iris as a biometric and performance. To determine the recognition performance of the iris image system digitized gray scale has been used and developed. Iris recognition system consists of the process of image segmentation, feature extraction which includes the RGB image to grayscale. Then, process transformation image to polar using Polar Transform and through the conversion process to form a histogram method LPQ and RILPQ. From this histogram, it will be analyzed to determine or recognize the iris image. To implement this recognition, the machine learning process has been implementing a database containing the iris image test database and iris image train database has been developed. K Nearest Neighbour classifier algorithm will use for recognition in the Iris Recognition System. An iris recognition system that requires the comprehension of a complex algorithm was succesfully developed and it is effective enough when being integrated with a system that requires identity checking. The overall system that deploy LPQ and RILPQ with histogram analysis was shown to achieve the initial objectives of this project. It was also proved to attain high recognition accuracy.

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