"Fast and efficent multimodal eye biometrics using projective dictionar" by Abhijit Das, Prabir Mondal et al.
 

Fast and efficent multimodal eye biometrics using projective dictionary pair learning

Document Type

Conference Article

Publication Title

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.

First Page

1402

Last Page

1408

DOI

10.1109/CEC.2016.7743953

Publication Date

11-14-2016

Comments

Open Access; Green Open Access

Link to Full Text

Share

COinS