Contrastive Uncertainty Learning for Iris Recognition with Insufficient Labeled Samples

Abstract

Cross-database recognition is still an unavoidable challenge when deploying an iris recognition system to a new environment. In the paper, we present a compromise problem that resembles the real-world scenario, named iris recognition with insufficient labeled samples. This new problem aims to improve the recognition performance by utilizing partially-or un-labeled data. To address the problem, we propose Contrastive Uncertainty Learning (CUL) by integrating the merits of uncertainty learning and contrastive self-supervised learning. CUL makes two efforts to learn a discriminative and robust feature representation. On the one hand, CUL explores the uncertain acquisition factors and adopts a probabilistic embedding to represent the iris image. In the probabilistic representation, the identity information and acquisition factors are disentangled into the mean and variance, avoiding the impact of uncertain acquisition factors on the identity information. On the other hand, CUL utilizes probabilistic embeddings to generate virtual positive and negative pairs. Then CUL builds its contrastive loss to group the similar samples closely and push the dissimilar samples apart. The experimental results demonstrate the effectiveness of the proposed CUL for iris recognition with insufficient labeled samples.