  This paper presents a deep-learned facial recognition method for underwater
robots to identify scuba divers. Specifically, the proposed method is able to
recognize divers underwater with faces heavily obscured by scuba masks and
breathing apparatus. Our contribution in this research is towards robust facial
identification of individuals under significant occlusion of facial features
and image degradation from underwater optical distortions. With the ability to
correctly recognize divers, autonomous underwater vehicles (AUV) will be able
to engage in collaborative tasks with the correct person in human-robot teams
and ensure that instructions are accepted from only those authorized to command
the robots. We demonstrate that our proposed framework is able to learn
discriminative features from real-world diver faces through different data
augmentation and generation techniques. Experimental evaluations show that this
framework achieves a 3-fold increase in prediction accuracy compared to the
state-of-the-art (SOTA) algorithms and is well-suited for embedded inference on
robotic platforms.
