  We present a novel architecture for person identification based on
typing-style, constructed of adaptive non-local spatio-temporal graph
convolutional network. Since type style dynamics convey meaningful information
that can be useful for person identification, we extract the joints positions
and then learn their movements' dynamics. Our non-local approach increases our
model's robustness to noisy input data while analyzing joints locations instead
of RGB data provides remarkable robustness to alternating environmental
conditions, e.g., lighting, noise, etc. We further present two new datasets for
typing style based person identification task and extensive evaluation that
displays our model's superior discriminative and generalization abilities, when
compared with state-of-the-art skeleton-based models.
