Abstract
Iris segmentation is an irreplaceable stage of iris recognition pipeline. The traditional segmentation methods are poorly robust, and the segmentation method using FCN runs very slowly. Therefore, in this paper, we propose an iris detection segmentation model based on multi-pysamid optimized Mask R-CNN. It is mainly realized by expanding the segmentation feature and performing the fusion operation on the segmentation feature obtained in the feature pyramid. This method enhances the expression of segmentation features and improves iris segmentation performance. Finally, experiments were conducted on two public datasets UBIRIS.v2 and CASIA.IrisV4-distance. Experimental results show that the proposed model achieves better results than state-of-the-art methods in the literature.
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Liang, H. et al. (2019). Multi-pyramid Optimized Mask R-CNN for Iris Detection and Segmentation. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_37
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DOI: https://doi.org/10.1007/978-3-030-31456-9_37
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