Abstract: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.