Abstract：Compared with previous designs, the one proposed in this paper has
three novelties. Firstly, iris texture is usually occluded by
eyelid, eyelash, et al. To eliminate these interference, a novel
feature descriptor is proposed -- only non-occluded region is
extracted by forming overlapping sectors in variable sizes.
Secondly, according to the distribution of the iris texture of each
sector, appropriate scale is selected for normalization so as to
solve the problem of 2D-Gabor filter's local optimum in a single
resolution. Thirdly, because of the normalization of different
scales, the overlapping region between the sectors are shown in
multi-resolution, so that a better extraction of the scale is
obtained which is not easy to be found in a single one. Comparison
is made between our algorithm and that of Daugman and Yao in
different iris databases. The experimental results show that the
proposed system can be more capable of efficient iris feature
extraction with a higher accuracy and a better robustness.
Guang Huo;Yuanning Liu;Xiaodong Zhu;Hongxing Dong. An Effective Iris Recognition System Based on Local Multi-resolution Feature Extraction[J]. , 2014, 11(11): 3695-3702.
Guang Huo;Yuanning Liu;Xiaodong Zhu;Hongxing Dong. An Effective Iris Recognition System Based on Local Multi-resolution Feature Extraction. , 2014, 11(11): 3695-3702.
2014, Vol. 11 
