摘要： Iris masks play an important role in iris recognition. They indicate which part of the iris texture map is useful and which part is occluded or contaminated by noisy image artifacts such as eyelashes, eyelids, eyeglasses frames, and specular reflections. The accuracy of the iris mask is extremely important. The performance of the iris recognition system will decrease dramatically when the iris mask is inaccurate, even when the best recognition algorithm is used. Traditionally, people used the rule-based algorithms to estimate iris masks from iris images. However, the accuracy of the iris masks generated this way is questionable. In this work, we propose to use Figueiredo and Jain's Gaussian Mixture Models (FJ-GMMs) to model the underlying probabilistic distributions of both valid and invalid regions on iris images. We also explored possible features and found that Gabor Filter Bank (GFB) provides the most discriminative information for our goal. Finally, we applied Simulated Annealing (SA) technique to optimize the parameters of GFB in order to achieve the best recognition rate. Experimental results show that the masks generated by the proposed algorithm increase the iris recognition rate on both ICE2 and UBIRIS dataset, verifying the effectiveness and importance of our proposed method for iris occlusion estimation. 其他題名： TPAMI 其他題名： IEEE Trans Pattern Anal Mach Intell 出版者： Los Alamitos, CA: IEEE 出版日期： 2013-04-01 出處： IEEE transactions on pattern analysis and machine intelligence, 2013-04, Vol.35 (4), p.784-796 資源來源： IEEE Electronic Library (IEL) 版權： 2015 INIST-CNRS 識別號： ISSN: 0162-8828 識別號： ISSN: 1939-3539 識別號： EISSN: 2160-9292 識別號： EISSN: 1939-3539 識別號： DOI: 10.1109/TPAMI.2012.169 識別號： PMID: 22868651 識別號： CODEN: ITPIDJ
