  In the past decades the set of human tasks that are solved by machines was
extended dramatically. From simple image classification problems researchers
now move towards solving more sophisticated and vital problems, like,
autonomous driving and language translation. The case of language translation
includes a challenging area of sign language translation that incorporates both
image and language processing. We make a step in that direction by proposing a
lightweight network for ASL gesture recognition with a performance sufficient
for practical applications. The proposed solution demonstrates impressive
robustness on MS-ASL dataset and in live mode for continuous sign gesture
recognition scenario. Additionally, we describe how to combine action
recognition model training with metric-learning to train the network on the
database of limited size. The training code is available as part of Intel
OpenVINO Training Extensions.
