关键词: 虹膜识别, 异质虹膜分割, 神经网络, 数据增强, 损失函数

Abstract:

In the iris recognition system, the segmentation of heterogeneous iris images（visible and infrared images） is the most important and challenging task. The difficulty of this task is that for heterogeneous iris images, both the accuracy and speed must be considered. This paper proposes a neural network model PI-Unet（Precise Iris Unet） suitable for heterogeneous iris segmentation, as well as data enhancement methods and loss functions for training the network model. The PI-Unet Encoder and Decoder are experimentally explored to obtain a network structure that can take into account both accuracy and speed. The data enhancement method and loss function proposed in this article are used to train the network. Using CASIA-iris -Intervel-v4 and UBIRIS.v2 iris image database to test the accuracy, parameter and calculation of the network. The test results show that the data enhancement method and loss function proposed in this paper can effectively improve the accuracy of heterogeneous iris segmentation. Compared with traditional iris segmentation algorithms and other iris segmentation neural networks, PI-Unet has better segmentation accuracy of heterogeneous iris images, less parameter and calculation, which can be applied to low-performance edge computing devices.

Key words: iris recognition, heterogeneous iris segmentation, neural network, data enhancement, loss function