Abstract
Iris recognition is a reliable method to protect the security of mobile devices. Low resolution (LR) iris images are inevitably acquired by mobile devices, which makes mobile iris recognition very challenging. This paper adopts two pixel level super-resolution (SR) methods: Super-Resolution Convolutional Neural Networks (SRCNN) and Super-Resolution Forests (SRF). The SR methods are conducted on the normalized iris images to recover more iris texture. Ordinal measures (OMs) are applied to extract robust iris features and the Hamming distance is used to calculate the matching score. Experiments are performed on two mobile iris databases. Results show that the pixel level SR technology has limited effectiveness in improving the iris recognition accuracy. The SRCNN and SRF methods get comparable recognition results. The SRF method is much faster at both the training and testing stage.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No. 61403389), the Beijing Nova Programme (Grant No. Z141101-001814090), and the Beijing Talents Fund (Grant No. 2015000021223ZK30).
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Zhang, Q., Li, H., He, Z., Sun, Z. (2016). Image Super-Resolution for Mobile Iris Recognition. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_44
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DOI: https://doi.org/10.1007/978-3-319-46654-5_44
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