Person identification using full-body motion and anthropometric biometrics from kinect videos
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
Robust periocular recognition by fusing local to holistic sparse representations
Proceedings of the 6th International Conference on Security of Information and Networks
Robust and efficient iris recognition based on sparse error correction model
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
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Noncontact biometrics such as face and iris have additional benefits over contact-based biometrics such as fingerprint and hand geometry. However, three important challenges need to be addressed in a noncontact biometrics-based authentication system: ability to handle unconstrained acquisition, robust and accurate matching, and privacy enhancement without compromising security. In this paper, we propose a unified framework based on random projections and sparse representations, that can simultaneously address all three issues mentioned above in relation to iris biometrics. Our proposed quality measure can handle segmentation errors and a wide variety of possible artifacts during iris acquisition. We demonstrate how the proposed approach can be easily extended to handle alignment variations and recognition from iris videos, resulting in a robust and accurate system. The proposed approach includes enhancements to privacy and security by providing ways to create cancelable iris templates. Results on public data sets show significant benefits of the proposed approach.