Abstract—Iris recognition has been demonstrated to be an efficient technology for doing personal identification. In this work, a method to perform iris recognition using biorthogonal wavelets is introduced. Effective use of biorthogonal waveletsusing a lifting technique to encode the iris information is demonstrated. This new method minimizes built in noise of iris images using in-band thresholding in order to provide better mapping and encoding of the relevant information. Comparison of Gabor encoding, similar to the method used by Daugman and others, and biorthogonal wavelet encoding is performed. While Daugman's approach is a well-proven algorithm, the effectiveness of our algorithm is shown for the CASIA database, based on the ability to classify inter and intra class distributions, and may provide more flexibility for non-ideal images. The method was tested on the CASIA dataset of iris images with over 4,536 intra-class and 566,244 inter-class comparisons made. After calculating Hamming distances and for the selected threshold value of 0.4, FRR and FAR values were 13.6% and 0.6% using Gabor filter and 0% and 0.03% using the biorthogonal wavelets.
Index Terms—iris recognition, biorthogonal wavelets, automatic segmentation, hamming distance, inter/intra class distribution, iris template, match score.
A. Abhyankar is with the Vishvakarma Institute of Information Technology, Pune, India. He is also associated with Clarkson University, NY, USA, and Government College of Engineering, Pune, India. E-mail: email@example.com, firstname.lastname@example.org
S. Schuckers is with the Clarkson University, NY, USA. She is also associated with West Virginia University, WV, USA
Cite: Aditya Abhyankar and Stephanie Schuckers, "Novel Biorthogonal Wavelet based Iris Recognition for Robust Biometric System," International Journal of Computer Theory and Engineering vol. 2, no. 2, pp. 233-237, 2010.