Abstract
Traditional iris recognition methods are mostly based on hand-crafted features, having limited success in less constrained scenarios due to non-ideal images caused by less cooperation of subjects. Though learned features via deep convolutional neural network (CNN) has shown remarkable success in computer vision field, it has been rarely used in the area of iris recognition. To tackle this issue, this paper proposes a novel method for robust iris recognition based on CNN models. As large-scale labeled iris images are not available, we design a lightweight CNN architecture suitable for iris datasets with small-scale labeled images. Different from existing works which use fully-connected features to capture the global texture, we propose to use the convolutional features for modeling local property and deformation of iris texture. We also develop a mechanism which can effectively combine the mask image for excluding the corrupted regions in the CNN model. The proposed method achieves much better performance than the compared methods on challenging ND-IRIS-0405 benchmark.
The work was supported by the National Natural Science Foundation of China (Grant No. 61471082).
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Tang, X., Xie, J., Li, P. (2017). Deep Convolutional Features for Iris Recognition. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_42
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DOI: https://doi.org/10.1007/978-3-319-69923-3_42
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