基于深度反卷积网络的虹膜定位方法研究
Iris Location Based on Deep Deconvolution Network
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摘要: 精确而快速地进行虹膜定位是有效地进行虹膜识别的重要前提。传统的虹膜定位方法有Daugman 定位法、Hough 变换定位法等, 但其对睫毛比较浓密、虹膜被遮挡较多等情况下的图片处理效果不是很好。文章在总结前人工作的基础上, 实现了一套基于深度学习的虹膜定位系统。该系统利用深度学习方法, 根据虹膜图像区域的特点, 对图像进行像素级分类。根据像素分类结果, 可以很好地标识虹膜区域和非虹膜区域, 达到定位识别虹膜区域的目的, 并在中国科学院自动化所公布的虹膜数据集 CASIA-IrisV3-Interval 上验证了文章工作的有效性, 像素分类精度达到约 98.4%, 达到了较高的鲁棒性。Abstract: Obtaining the iris localization precisely and fast is the prelude of effective iris recognition. Traditional iris localization methods, including Daugman localization and Hough transformation localization, are weak in processing images with thick eyelashes and severely shlter. In this paper, combined with previous work of other researchers, deep learning method was employed to classify iris images based on the characteristics of iris region. We carried our experiment on the CASIA-IrisV3-Interval dataset, to verify the effectiveness of our work. The accuracy of pixel-wise classification is around 98.4%, with a higher robustness.