Iris Recognition Based on Fine-Tune SquIrisNet

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.