| bibtype |
J -
Journal Article
|
| ARLID |
0444723 |
| utime |
20240103210152.3 |
| mtime |
20150625235959.9 |
| WOS |
000353350200008 |
| SCOPUS |
84939997157 |
| DOI |
10.1016/j.patrec.2015.02.012 |
| title
(primary) (eng) |
Unsupervised detection of non-iris occlusions |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0257389 |
| ISSN |
0167-8655 |
| title
|
Pattern Recognition Letters |
| volume_id |
57 |
| volume |
5 (2015) |
| page_num |
60-65 |
| publisher |
|
|
| keyword |
Iris recognition |
| keyword |
Color |
| keyword |
Markov random field |
| keyword |
Texture |
| author
(primary) |
| ARLID |
cav_un_auth*0101093 |
| name1 |
Haindl |
| name2 |
Michal |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept (eng) |
Department of Pattern Recognition |
| department (cz) |
RO |
| department (eng) |
RO |
| institution |
UTIA-B |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0292156 |
| name1 |
Krupička |
| name2 |
Mikuláš |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept |
Department of Pattern Recognition |
| department (cz) |
RO |
| department |
RO |
| institution |
UTIA-B |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA14-10911S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0303439 |
|
| abstract
(eng) |
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. |
| reportyear |
2016 |
| RIV |
BD |
| num_of_auth |
2 |
| mrcbC52 |
4 A hod 4ah 20231122141013.3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0247504 |
| mrcbC64 |
1 Department of Pattern Recognition UTIA-B 10201 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE |
| confidential |
S |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE |
| mrcbT16-j |
0.734 |
| mrcbT16-s |
0.950 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
60.575 |
| mrcbT16-C |
55.000 |
| mrcbT16-D |
Q2 |
| mrcbT16-E |
Q2 |
| arlyear |
2015 |
| mrcbTft |
\nSoubory v repozitáři: haindl-0444723.pdf |
| mrcbU14 |
84939997157 SCOPUS |
| mrcbU34 |
000353350200008 WOS |
| mrcbU63 |
cav_un_epca*0257389 Pattern Recognition Letters 0167-8655 1872-7344 Roč. 57 č. 5 2015 60 65 Elsevier |
|