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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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