基于多任务卷积神经网络的虹膜图像质量评估方法

An Iris Image Quality Evaluation Method Based on Multi-task Convolutional Neural Network

为了自动、准确、高效地评估采集图像的质量,设计了一个名为MTIQA的卷积神经网络。该网络能够输出与主观评价指标保持较高一致性的客观评估结果。MTIQA采用多任务学习策略,包含网络训练质量评估和失真类型分类两个任务,将两个任务的损失融合并构成新的损失函数。为了评估算法所得到的客观指标的可靠性,建立了名为SIR2019的单眼虹膜质量评估数据集,并召集志愿者进行主观实验以得到主观评价指标。在SIR2019和CASIA-Iris-Distance-Lamp数据集上的实验结果表明,该网络在虹膜图像质量评估上具有较好的可行性、准确性和鲁棒性。

In order to automatically,accurately and efficiently evaluate the quality of collected images,a convolutional neural network named MTIQA is designed,which can output objective evaluation results with high consistency with subjective evaluation indexes.MTIQA adopts the multi-task learning strategy,classifies the two tasks through a network training quality assessment and distortion type,and finally fuses the losses of the two tasks into a new loss function.In order to evaluate the reliability of the objective indexes obtained by the algorithm,a monocular iris quality evaluation dataset named SIR2019 is established,and volunteers are recruited for subjective experiments to obtain the subjective evaluation indexes.The proposed network is tested on SIR2019 and CASIA-Iris-Distance-Lamp datasets.The experimental results show that the proposed network has good accuracy,robustness and feasibility in iris image quality evaluation.