Iris recognition system consists of image acquisition, iris preprocessing, iris segmentation and feature extraction with comparism (matching) stages. The biometric based personal identification using iris requires accurate iris segmentation for successful identification or recognition. Recently, several researchers have implemented various methods for segmentation of boundaries which will require a modification of some of the existing segmentation algorithms for their proper recognition. Therefore, this research presents a 2D Wavelet Transform and Chi-squared model for iris features extraction and recognition. Circular Hough Transform was used for the segmentation of the iris image. The system localizes the circular iris and pupil region and removes the occluding eyelids and eyelashes. The extracted iris region is normalized using Daugman’s rubber sheet model into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data (iris signature) from the 2D wavelet transform data is extracted, forming the biometric template. The chi-squared distance is employed for classification of iris templates and recognition. Implementing this model can enhance identification. Based on the designed system, an FAR (False Acceptances ratio) of 0.00 and an FRR (False Rejection Ratio) of 0.896 was achieved.
Performance Analysis of Iris Recognition using Multi Stage Wavelet Transform Decomposition and Bicubic Interpolation Technique
