关键词: 虹膜分割, 深度学习, 虹膜识别, 小样本, 轻量级, 注意力机制

Abstract: Aiming at the problem that complex segmentation networks could not converge on small sample iris datasets, we proposed an iris segmentation model based on lightweight convolutional neural network. Firstly, the model used a feature extraction module based on depth-wise separable convolution to extract iris image features, which could  significantly reduce model parameters while maintaining segmentation accuracy. Secondly, an efficient attention mechanism module was introduced between the encoder and the decoder, which could effectively obtain rich context information and improve the discriminability of iris region pixels. Finally, the experimental results on the iris database UBIRIS.V2 show that the proposed method not only has significant performance advantages on small sample databases, but also has high segmentation accuracy on large sample databases.

Key words: iris segmentation, deep learning, iris recognition, small sample, lightweight, attention mechanism