doi:10.1109/LRA.2021.3101822engSabater, A.Alonso, I.Montesano, L.Murillo, A.C.Domain and View-Point Agnostic Hand Action RecognitionART-2021-126154Hand action recognition is a special case of action recognition with applications in human-robot interaction, virtual reality or life-logging systems. Building action classifiers able to work for such heterogeneous action domains 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. When working on a single domain (intra-domain action classification) our approach performs better or similar to current state-of-the-art methods on well-known hand action recognition benchmarks. And, more importantly, when performing hand action recognition for action domains and camera perspectives which our approach has not been trained for (cross-domain action classification), our proposed framework achieves comparable performance to intra-domain state-of-the-art methods. These experiments show the robustness and generalization capabilities of our framework.2021http://zaguan.unizar.es/record/15027010.1109/LRA.2021.3101822http://zaguan.unizar.es/record/150270oai:zaguan.unizar.es:150270info:eu-repo/grantAgreement/ES/DGA-FSE/T45-17Rinfo:eu-repo/grantAgreement/ES/MICIU-AEI-FEDER/PGC2018-098817-A-I00IEEE Robotics and Automation Letters 6, 4 (2021), 7823-7830All rights reservedhttp://www.europeana.eu/rights/rr-f/info:eu-repo/semantics/openAccess