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Smartphone based visible iris recognition using deep sparse filtering

机译:使用深度稀疏过滤的基于智能手机的可见虹膜识别

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Good biometric performance of iris recognition motivates it to be used for many large scale security and access control applications. Recent works have identified visible spectrum iris recognition as a viable option with considerable performance. Key advantages of visible spectrum iris recognition include the possibility of iris imaging in on-the-move and at-a-distance scenarios as compared to fixed range imaging in near-infrared light. The unconstrained iris imaging captures the images with largely varying radius of iris and pupil. In this work, we propose a new segmentation scheme and adapt it to smartphone based visible iris images for approximating the radius of the iris to achieve robust segmentation. The proposed technique has shown the improved segmentation accuracy up to 85% with standard OSIRIS v4.1. This work also proposes a new feature extraction method based on deep sparse filtering to obtain robust features for unconstrained iris images. To evaluate the proposed segmentation scheme and feature extraction scheme, we employ a publicly available database and also compose a new iris image database. The newly composed iris image database (VSSIRIS) is acquired using two different srnartphones - iPhone 5S and Nokia Lumia 1020 under mixed illumination with unconstrained conditions in visible spectrum. The biornetric performance is benchmarked based on the equal error rate ([ER) obtained from various state-of-art schemes and proposed feature extraction scheme. An impressive EER of 1.62% is obtained on our VSSIRIS database and an average gain of around 2% in EER is obtained on the public database as compared to the well-known state-of-art schemes. (C) 2014 Elsevier B.V. All rights reserved.
机译:虹膜识别的良好生物识别性能促使它被用于许多大规模的安全性和访问控制应用程序。最近的工作已将可见光谱虹膜识别确定为具有相当性能的可行选择。可见光谱虹膜识别的主要优势包括与近红外光中的固定范围成像相比,在移动和远距离场景中进行虹膜成像的可能性。不受约束的虹膜成像可捕获虹膜和瞳孔半径变化很大的图像。在这项工作中,我们提出了一种新的分割方案,并将其应用于基于智能手机的可见虹膜图像,以近似虹膜的半径来实现鲁棒的分割。所提出的技术显示,使用标准OSIRIS v4.1可以将分割精度提高到85%。这项工作还提出了一种新的基于深度稀疏滤波的特征提取方法,以获得不受约束的虹膜图像的鲁棒特征。为了评估提出的分割方案和特征提取方案,我们使用了一个公开可用的数据库,并且还组成了一个新的虹膜图像数据库。新的虹膜图像数据库(VSSIRIS)是使用两种不同的srnartphone(iPhone 5S和诺基亚Lumia 1020)在可见光不受限制的混合照明条件下获取的。基于从各种最新方案和提出的特征提取方案中获得的均等错误率(ER)来对生物力学性能进行基准测试。与众所周知的最新方案相比,在我们的VSSIRIS数据库上获得了令人印象深刻的1.62%的EER,在公共数据库上获得了约2%的EER平均收益。 (C)2014 Elsevier B.V.保留所有权利。

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