Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation
Importance of biometric user identification is increasing everyday. One of the most promising techniques is the one based on the human iris. The authors, in this work, describe different approaches to develop this biometric technique. Based on the works carried out by Daugman, the authors have worked using Gabor filters and Hamming distance. But in addition, they have also worked in zero-crossing representation of the dyadic wavelet transform applied to two different iris signatures: one based on a single virtual circle of the iris; the other one based on an annular region. Also other metrics have been applied to be compared with the results obtained with the Hamming distance. In this work Euclidean distance and dZ d Z will be shown. The last proposed approach is translation, rotation and scale invariant. Results will show a classification success up to 99.6% achieving an equal error rate down to 0.12% 0.12 % and the possibility of having null false acceptance rates with very low false rejection rates.
gabor filters zero-crossing representation biometric identification human iris pattern discrete dyadic wavelet transform multiscale analysis