Novel Biorthogonal Wavelet based Iris Recognition for Robust Biometric System


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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 wavelets
using 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.



INTRODUCTION
Biometric identification is gaining more popularity and
more acceptance in public as well as in private sectors. Iris
recognition is considered to be highly accurate and reliable
method of biometric identification. The iris, being found to
be very stable, highly unique and easy to capture, is classified
as one of the better biometric identifiers [1,2].
The unique epigenetic patterns of a human iris are used for
personal identification. Image processing and signal
processing techniques are employed to extract information
from unique iris structure from a digitized image of an eye
[3,4,5,6]. This information is encoded to formulate a
``biometric template'', which is stored in a database and also
used for identification.


III. RESULTS
753 out of 756 iris images were segmented accurately for
CASIA data set and 77 out of 80 iris images were segmented
accurately for eye-center data set. The images with
inaccurately segmented iris region are not used for further
analysis. The matching scores are divided into inter-class and
intra-class matching.