Underwater human-robot communication remains an open challenge, particularly when robots are required to accept instructions from only a few select individuals wearing similar outfits. Previous solutions ,  leverage different swimming patterns to uniquely identify different scuba divers, however, these algorithms are inapplicable for close-proximity interactions as they require the entire body to be visible and in motion.
To this end, we propose a framework to identify scuba divers underwater using facial recognition. A diver face is usually heavily obscured by scuba masks and breathing apparatus. As a result, state-of-the-art face recognition algorithms ,  struggle to extract robust features from the diver faces. We address this issue by developing a data augmentation technique to create realistic diver faces from regular (non-diver) faces and construct a dataset that includes both diver and non-diver faces. We use this dataset to learn highly discriminative features representing the diver faces. Then, we create a database which holds all the authorized users' diver face representations which are extracted using the previously learnt model which can be queried during real-time inference. Fig. 1 demonstrates the framework.
Fig. 1: Demonstration of a diver face identification system for underwater human-robot interaction. The framework detects diver faces underwater and extracts discriminative feature embeddings which are matched against pre-computed embeddings of authorized users stored in a database.
We validate the effectiveness of the proposed system through qualitative and quantitative experiments and compare the results with several state-of-the-art algorithms' performances. We also analyze the practical feasibility of this framework for mobile platforms. Detailed information can be found in the paper.
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