Iris Center Localization Using Geodesic Distance and CNN
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10244291" target="_blank" >RIV/61989100:27240/19:10244291 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-31321-0_7" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-31321-0_7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-31321-0_7" target="_blank" >10.1007/978-3-030-31321-0_7</a>
Alternative languages
Result language
angličtina
Original language name
Iris Center Localization Using Geodesic Distance and CNN
Original language description
In this paper, we propose a new eye iris center localization method for remote tracking scenarios. The method combines the geodesic distance with CNN-based classification. Firstly, the geodesic distance is used for fast preliminary localization of the regions possibly containing the iris. Then a convolutional neural network is used to carry out the final decision and to refine the final position of the iris center. In the first step, the areas that do not appear to contain the eyeball are quickly filtered out, which makes the whole algorithm fast even on less powerful computers. The proposed method is evaluated and compared with the state-of-the-art methods on two publicly available datasets focused to the remote tracking scenarios (namely BioID [9], GI4E [15]). (C) 2019, Springer Nature Switzerland AG.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN
978-3-030-31320-3
ISSN
0302-9743
e-ISSN
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Number of pages
10
Pages from-to
76-85
Publisher name
Springer
Place of publication
Cham
Event location
Madrid
Event date
Jul 1, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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