Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/25269
Title: Local descriptors fusion for mobile iris verification
Authors: Aginako, Naiara
Martínez Otzeta, Jose María
Sierra, Basilio
Castrillón-Santana, Modesto 
Lorenzo Navarro, José Javier 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Biometrics
Iris verification
Issue Date: 2016
Journal: Proceedings - International Conference on Pattern Recognition 
Conference: 23rd International Conference on Pattern Recognition (ICPR) 
23rd International Conference on Pattern Recognition, ICPR 2016 
Abstract: This paper summarizes the proposal submitted by the joint team conformed by researchers from UPV and ULPGC to the Mobile Iris CHallenge Evaluation II. The approach makes use of a state-of-the-art iris segmentation technique, to later extract features making use of local descriptors. Those suitable to the problem are selected after evaluating a collection of 15 local descriptors, covering a range of different grid configuration setups. A Machine Learning approach is used, learning a supervised classifier to deal with the descriptors data. A classifier is obtained for each descriptor, and the best ones are combined in a multi-classifier system. The final step fuses the classifier outputs obtained for 5 different local descriptors, to compute the dissimilarity measure for a pair of iris images.
URI: http://hdl.handle.net/10553/25269
ISBN: 9781509048472
ISSN: 1051-4651
DOI: 10.1109/ICPR.2016.7899627
Source: International Conference on Pattern Recognition [ISSN 1051-4651], article number 7899627, p. 165-169
Appears in Collections:Actas de congresos
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