Abstract-Non-contact 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 non-contact 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 the 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 datasets show significant benefits of the proposed approach.

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Abstract

While recent techniques for discriminative dictionary learning have demonstrated tremendous success in image analysis applications, their performance is often limited by the amount of labeled data available for training. Even though labeling images is difficult, it is relatively easy to collect unlabeled images either by querying the web or from public datasets. In this paper, we propose a discriminative dictionary learning technique which utilizes both labeled and unlabeled data for learning dictionaries. Extensive evaluation on existing datasets demonstrate that the proposed method performs significantly better than state of the art dictionary learning approaches when unlabeled images are available for training.

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