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We present a novel architecture for person identification based ontyping-style, constructed of adaptive non-local spatio-temporal graphconvolutional network. Since type style dynamics convey meaningful informationthat can be useful for person identification, we extract the joints positionsand then learn their movements' dynamics. Our non-local approach increases ourmodel's robustness to noisy input data while analyzing joints locations insteadof RGB data provides remarkable robustness to alternating environmentalconditions, e.g., lighting, noise, etc. We further present two new datasets fortyping style based person identification task and extensive evaluation thatdisplays our model's superior discriminative and generalization abilities, whencompared with state-of-the-art skeleton-based models.

Skeleon-Based Typing Style Learning For Person Identification