0444723 20240103210152.320150625235959.9 000353350200008 84939997157 10.1016/j.patrec.2015.02.012 6 s. cav_un_epca*02573890167-8655575 (2015)60-65Elsevier Iris recognition Color Markov random field Texture cav_un_auth*0101093 Haindl Michal Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0292156 Krupička Mikuláš Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2015/RO/haindl-0444723.pdf GA14-10911S GA ČR CZ cav_un_auth*0303439 This paper presents a fast precise unsupervised iris defects detection method based on the underlying multispectral spatial probabilistic iris textural model and adaptive thresholding applied to demanding high resolution mobile device measurements. The accurate detection of iris eyelids and reflections is the prerequisite for the accurate iris recognition, both in near-infrared or visible spectrum measurements. The model adaptively learns its parameters on the iris texture part and subsequently checks for iris reflections using the recursive prediction analysis. The method is developed for color eye images from unconstrained mobile devices but it was also successfully tested on the UBIRIS v2 eye database. Our method ranked first from the 97+1 recent Noisy Iris Challenge Evaluation contest alternative methods on this large color iris database using the exact contest data and methodology. 2016 BD 2 4 A hod 4ah 20231122141013.3 RVO:67985556 http://hdl.handle.net/11104/0247504 1 Department of Pattern Recognition UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE S COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE 2.002 0.25 8.5 0.01511 0.734 7671 0.950 Q1 1.471 256 Q2 60.575 55 Q2 Q2 55 2015 Soubory v repozitáři: haindl-0444723.pdf 84939997157 SCOPUS 000353350200008 WOS cav_un_epca*0257389 Pattern Recognition Letters 0167-8655 1872-7344 Roč. 57 č. 5 2015 60 65 Elsevier