A Lightweight Semi-supervised Eye Semantic Segmentation Method Based on Iterative Learning

Abstract

Eye semantic segmentation has a significant role in promoting biometric recognition. Most works improve the accuracy through designing complicated feature learning modules that usually require a high computation cost and are prone to over-fit. In this work, we propose a lightweight eye semantic segmentation method based on iterative learning, which only requires a small amount of labeled eye image data under visible light conditions. To be specific, we first propose a pseudo-label filtering strategy for eye semantic segmentation, which leverages a semi-supervised incremental learning technique to solve the problem of label scarcity. Besides, an attention-guided lightweight high-representation module is designed to boost precision while ensuring efficiency. Experiments on the Ubiris dataset show that the proposed method achieves state-of-the-art performance, with a mIoU of 86.18%.