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 
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
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

Files in This Item:
File Description SizeFormat 
C101_ICPR16_preprint.pdfpreprint833,6 kBAdobe PDFView/Open
Show full item record

Page view(s)

checked on Nov 16, 2019


checked on Nov 16, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.