• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors
【2h】

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

机译：IrisDenseNet：通过可见光和近红外摄像头传感器在图像中使用密集连接的全卷积网络进行稳健的虹膜分割

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR) light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN) has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE-I) datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA) v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets.
机译：计算机视觉的最新进展为在移动和手持设备中部署生物识别算法开辟了新的视野。同样，在不受约束的情况下，现在非常需要准确地进行虹膜识别。这些环境使所获取的虹膜图像呈现出遮挡，低分辨率，模糊，异常闪烁，重影效果和斜角。主流的分割算法无法应对这些约束。此外，由于无法使用近红外（NIR）光，可见光环境中的虹膜识别使虹膜分割面临可见光噪声的挑战。卷积神经网络（CNN）的深度学习在各种应用中带来了相当大的突破。为了解决可见光和近红外摄像头传感器在挑战性情况下的虹膜分割问题，本文提出了一种紧密连接的全卷积网络（IrisDenseNet），即使使用劣质图像，也可以通过使用更好的信息来确定真实虹膜边界密集块之间的梯度流。在进行的实验中，使用了五个可见光和近红外环境的数据集。对于可见光环境，使用了嘈杂的虹膜挑战评估第二部分（从UBIRIS.v2数据库中选择NICE-II）和移动虹膜挑战评估（MICHE-I）数据集。对于NIR环境，使用了自动化研究所，中国科学院（CASIA）v4.0间隔，CASIA v4.0距离和IIT Delhi v1.0虹膜数据集。实验结果表明，针对所有五个数据集，提出的IrisDenseNet的最佳分割效果和优于现有算法的出色性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号