Real-time iris segmentation model based on lightweight convolutional neural network

Abstract

Iris segmentation is an important task in the iris recognition system. Traditional iris segmentation methods are susceptible to irrelevant noise. At present, segmentation methods based on convolutional neural networks (CNNs) have become the main trend in addressing the task. Although these networks achieve promising segmentation performance on the advanced graphics processor, they require a lot of computation and time, which are far from meeting the standards of real-time iris segmentation. To this end, a precise and fast iris segmentation algorithm is proposed. First, an efficient feature extraction network that combines depth-wise separable convolution with dilated convolution is designed to reduce model parameters while maintaining segmentation accuracy. Then, an attention mechanism is introduced to suppress noise interference and enhance the discriminability of the iris region. Finally, an auxiliary training branch is proposed to overcome the vanishing gradient problem. Experimental results show that the proposed method not only achieves state-of-the-art performance but also is more efficient in terms of required parameters, calculated load, and storage space. Specifically, the model parameter is only 0.1 million (M), and the average prediction time is <0.03 seconds (S).