  Hand action recognition is a special case of human action recognition with
applications in human robot interaction, virtual reality or life-logging
systems. Building action classifiers that are useful to recognize such
heterogeneous set of activities is very challenging. There are very subtle
changes across different actions from a given application but also large
variations across domains (e.g. virtual reality vs life-logging). This work
introduces a novel skeleton-based hand motion representation model that tackles
this problem. The framework we propose is agnostic to the application domain or
camera recording view-point. We demonstrate the performance of our proposed
motion representation model both working for a single specific domain
(intra-domain action classification) and working for different unseen domains
(cross-domain action classification). For the intra-domain case, our approach
gets better or similar performance than current state-of-the-art methods on
well-known hand action recognition benchmarks. And when performing cross-domain
hand action recognition (i.e., training our motion representation model in
frontal-view recordings and testing it both for egocentric and third-person
views), our approach achieves comparable results to the state-of-the-art
methods that are trained intra-domain.
