IrisGuideNet: Guided Localization and Segmentation Network for Unconstrained Iris Biometrics
Jawad Muhammad, Caiyong Wang, Yunlong Wang, Kunbo Zhang, Zhenan Sun
Abstract
In recent years, unconstraint iris biometric is becoming more prevalent due to its wide range of user applications. But since it allows less user co-operation, it presents numerous challenges to the iris preprocessing task of localisation and segmentation (ILS). To address these challenges, many ILS techniques have been proposed with the deep learning CNN based approaches been the most effective. Training the CNN is data intensive and most of the existing CNN based ILS adopt general purpose CNN without any iris specific guidance. However, the available iris dataset comprises of small subsets with labelled images. As such, the existing CNN models can be less effective as they are trained with these dataset. Hence, in this paper, we propose a guided CNN based ILS technique termed IrisGuideNet by incorporating known iris specific heuristics into the network pipeline. IrisGuideNet has an encoder-decoder structure designed to be invariant to translation and rotation and can capture iris at multiple scales. To address the iris limited data problem, unlike the existing CNN based ILS, during the training process, we adopt the deep supervision technique, employ hybrid losses and introduce a novel iris specific heuristics named Iris Regularization Term (IRT) in other to effectively train the network. At inference, we introduce a novel Iris Infusion Module (IIM) that utilise the geometrical relationships between the ILS outputs to refine the predicted outputs through logical operations. Our models were trained and evaluated with the recently published NIR-ISL Challange * datasets and has proven to be effective as it has outperformed most of the participating models across all the database categories in the competition.