Abstract

With the development of iris recognition technology, sensors of iris images acquisition are being constantly developed and updated. Re-register users every time a new sensor is deployed is time-consuming and complicated, especially in applications with large-scale registered users. Therefore, it is a challenging problem to choose the common recognition model which is effective for multi-source heterogeneous iris recognition(MSH-IR). The paper proposes a efficient neural network model of stacked Convolutional Deep Belief Networks-Deep Belief Network (CDBNs-DBN) for MSH-IR. The main improvements are two parts: firstly, this model uses the region-by-region extraction method and positions the convolution kernel through the offset of the hidden layer to locate the effective local texture feature structure. Secondly, the model uses DBN as a classifier in order to reduce the reconstruction error through the negative feedback mechanism of the auto-encoder. Experimental results have been implemented on publicly available IIT Delhi iris database, which is captured by three different iris captured sensors. Experiments shows the model performs strong robustness performance and recognition ability.

中文翻译：

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堆叠卷积深度置信网络-深度置信网络模型的多源异构虹膜识别

摘要

随着虹膜识别技术的发展，虹膜图像采集传感器正在不断发展和更新。每次部署新传感器时都要重新注册用户既耗时又复杂，尤其是在具有大规模注册用户的应用程序中。因此，选择对多源异构虹膜识别（MSH-IR）有效的通用识别模型是一个具有挑战性的问题。提出了一种用于MSH-IR的高效的卷积深度信念网络-深度信念网络（CDBNs-DBN）神经网络模型。主要的改进有两个部分：首先，该模型使用逐区域提取方法，并通过隐藏层的偏移定位卷积核，以定位有效的局部纹理特征结构。第二，该模型使用DBN作为分类器，以通过自动编码器的负反馈机制减少重构误差。实验结果已在公开的IIT德里虹膜数据库上实现，该数据库由三个不同的虹膜捕获传感器捕获。实验表明该模型具有很强的鲁棒性和识别能力。