Abstract

Convolutional neural networks (CNN) have achieved unprecedented results in the fields of pattern recognition and image processing. CNN methods have also been gradually applied to iris recognition. However, in the case of insufficient training samples, training deep CNN models is prone to overfitting. In order to solve the above problem, this paper proposes an iris recognition method based on fine-tune SquIrisNet. When the pre-trained SqueezNet is migrated to the iris dataset, the fully connected layers are used instead of the convolutional layer and the global pooling layer, then the parameters are adjusted using the error back propagation algorithm, and finally the images are classified by the Softmax classifier. The fully connected layers added in this model can play the role of “firewall” in the fine-tune process, retaining the model complexity to a certain extent. The experimental results on the IIT Delhi and SDUMLA-HMT iris databases show that the proposed method has higher correct recognition rate, faster convergence speed and better robustness than AlexNet and VGGNet.

中文翻译：

---

基于微调SquIrisNet的虹膜识别

摘要

卷积神经网络（CNN）在模式识别和图像处理领域取得了空前的成果。CNN方法也已逐渐应用于虹膜识别。但是，在训练样本不足的情况下，训练深度CNN模型很容易过度拟合。为了解决上述问题，本文提出了一种基于微调SquIrisNet的虹膜识别方法。当将预训练的SqueezNet迁移到虹膜数据集时，将使用全连接层而不是卷积层和全局池化层，然后使用误差反向传播算法调整参数，最后通过图像对图像进行分类。 Softmax分类器。在此模型中添加的完全连接的层可以在微调过程中扮演“防火墙”的角色，在一定程度上保留了模型的复杂性。在IIT Delhi和SDUMLA-HMT虹膜数据库上的实验结果表明，与AlexNet和VGGNet相比，该方法具有更高的正确识别率，更快的收敛速度和更好的鲁棒性。