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首页> 外文会议>IEEE International Conference on Image Processing >DeepIrisNet: DEEP IRIS REPRESENTATION WITH APPLICATIONS IN IRIS RECOGNITION AND CROSS-SENSOR IRIS RECOGNITION
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DeepIrisNet: DEEP IRIS REPRESENTATION WITH APPLICATIONS IN IRIS RECOGNITION AND CROSS-SENSOR IRIS RECOGNITION

机译：Deepirisnet：深度虹膜识别在虹膜识别和交叉传感器虹膜识别中的应用

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摘要

Despite significant advances in iris recognition (IR), the efficient and robust IR at scale and in non-ideal conditions presents serious performance issues and is still ongoing research topic. Deep Convolution Neural Networks (DCNN) are powerful visual models that have reported state-of-the-art performance in several domains. In this paper, we propose deep learning based method termed as DeepIrisNet for iris representation. The proposed approach bases on very deep architecture and various tricks from recent successful CNNs. Experimental analysis reveal that proposed DeepIrisNet can model the micro-structures of iris very effectively and provides robust, discriminative, compact, and very easy-to-implement iris representation that obtains state-of-the-art accuracy. Furthermore, we evaluate our iris representation for cross-sensor IR. The experimental results demonstrate that DeepIrisNet models obtain a significant improvement in cross-sensor recognition accuracy too.
机译：尽管在虹膜识别（IR）中有重大进展，但规模和非理想条件的高效和强大的IR呈现出严重的绩效问题，并且仍然是正在进行的研究主题。深度卷积神经网络（DCNN）是强大的视觉模型，这些模型在若干域中报告了最先进的性能。在本文中，我们提出基于深度学习的方法被称为虹膜代表的Deepirisnet。拟议的方法基于近期成功CNN的非常深的架构和各种技巧。实验分析表明，建议的Deepirisnet可以非常有效地模拟虹膜的微结构，并提供稳健，辨别，紧凑，非常易于实现最先进的准确性的虹膜表示。此外，我们评估了跨传感器IR的虹膜表示。实验结果表明，Deepirisnet模型也获得了交叉传感器识别精度的显着改善。

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