Iris Recognition Under Partial Occlusion Based on Non-negative Sparse Representation Classification

DOI

10.2991/msota-16.2016.92How to use a DOI?

Keywords

iris recogniton; sparse representation;partial occlusion; non-negative; log-gabor feature

Abstract

To improve the reliability and accuracy of personal identification based on iris under partial occlusion, this paper proposed a non-negative dictionary sparse representation and classification scheme for iris recognition. The non-negative dictionary includes the Log-Gabor feature dictionary extracted from normalized iris image. The use of Log-Gabor makes the occlusion dictionary compressible, and can reduce the computational cost. Experiments on UBIRIS iris database demonstrated the effectiveness of the proposed Log-Gabor based non-negative sparse representation classification.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Fenghua Wang
AU  - Qiumei Zheng
AU  - Shaoshu Gao
PY  - 2016/12
DA  - 2016/12
TI  - Iris Recognition Under Partial Occlusion Based on Non-negative Sparse Representation Classification
BT  - Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)
PB  - Atlantis Press
SP  - 417
EP  - 420
SN  - 2352-538X
UR  - https://doi.org/10.2991/msota-16.2016.92
DO  - 10.2991/msota-16.2016.92
ID  - Wang2016/12
ER  -