论文标题
复杂值的IRIS识别网络
Complex-valued Iris Recognition Network
论文作者
论文摘要
在这项工作中，我们为虹膜识别的任务设计了一个完全复杂的价值神经网络。与一般对象识别的问题不同，可以使用实值的神经网络来提取相关特征，虹膜识别取决于从输入虹膜纹理中提取相位和幅度信息，以便更好地表示其生物识别内容。这需要进行相位信息的提取和处理，这些信息无法通过实现的神经网络有效地处理。在这方面，我们设计了一个完全复杂的神经网络，可以更好地捕获虹膜纹理的多尺度，多分辨率和多取向阶段和振幅特征。我们展示了与Gabor小波一起用于生成经典iriscode的Gabor小波的提议的复合物值虹膜识别网络的强烈对应。但是，提出的方法可实现为虹膜识别而定制的自动复合物值学习的新功能。我们在三个基准数据集上进行实验-ND-CrossSensor-2013，Casia-iris-iris-theys和ubiris.v2-，并显示了拟议网络对IRIS识别任务的好处。我们利用可视化方案来传达复杂价值的网络与标准实价网络相比如何提取与虹膜纹理的根本不同特征。
In this work, we design a fully complex-valued neural network for the task of iris recognition. Unlike the problem of general object recognition, where real-valued neural networks can be used to extract pertinent features, iris recognition depends on the extraction of both phase and magnitude information from the input iris texture in order to better represent its biometric content. This necessitates the extraction and processing of phase information that cannot be effectively handled by a real-valued neural network. In this regard, we design a fully complex-valued neural network that can better capture the multi-scale, multi-resolution, and multi-orientation phase and amplitude features of the iris texture. We show a strong correspondence of the proposed complex-valued iris recognition network with Gabor wavelets that are used to generate the classical IrisCode; however, the proposed method enables a new capability of automatic complex-valued feature learning that is tailored for iris recognition. We conduct experiments on three benchmark datasets - ND-CrossSensor-2013, CASIA-Iris-Thousand and UBIRIS.v2 - and show the benefit of the proposed network for the task of iris recognition. We exploit visualization schemes to convey how the complex-valued network, when compared to standard real-valued networks, extracts fundamentally different features from the iris texture.
