Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/41876
Title: Fast and efficent multimodal eye biometrics using projective dictionary pair learning
Authors: Das, Abhijit
Mondal, Prabir
Pal, Umapada
Ferrer, Miguel Angel 
Blumenstein, Michael
UNESCO Clasification: 120325 Diseño de sistemas sensores
Keywords: Biometrics
Dictionary Learning
Iris
Multimodal Biometrics
Sclera
Synthesis Dictionary
Issue Date: 2016
Journal: 2016 IEEE Congress on Evolutionary Computation, CEC 2016
Conference: 2016 IEEE Congress on Evolutionary Computation, CEC 2016 
Abstract: This work proposes a projective pairwise dictionary learning-based approach for fast and efficient multimodal eye biometrics. The work uses a faster Projective pairwise Discriminative Dictionary Learning (DL) in contrast to the traditional DL which uses synthesis DL. Projective Pairwise Discriminative Dictionary (PPDD) uses a synthesis dictionary and an analysis dictionary jointly to achieve the goal of pattern representation and discrimination. As the PPDD process of DL is in contrast to the use of l0 or l1-norm sparsity constraints on the representation coefficients adopted in most traditional DL, it works faster than other DL. Moreover, the blending of synthesis dictionary and an analysis dictionary also enhance the feature representation of the complex eye patterns. We employed the combination of sclera and iris traits to establish multimodal biometrics. The experimental study and analysis conducted fulfill the hypothesis we considered. In this work we employed a part of the UBIRIS version 1 dataset to conduct the experiments.
URI: http://hdl.handle.net/10553/41876
ISBN: 9781509006229
DOI: 10.1109/CEC.2016.7743953
Source: 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (7743953), p. 1402-1408
Appears in Collections:Actas de congresos
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