Abstract
Hand gesture recognition has become an important component in many modern applications such as human–computer interaction, virtual environments, and sign language interpretation. Most existing gesture recognition systems operate under a closed-set assumption, where the gesture categories used during testing are the same as those present in the training stage. However, this assumption is often unrealistic in real-world environments, where systems may encounter new gesture types or variations that were not previously observed. To overcome this limitation, the concept of open-set hand gesture recognition has gained increasing attention. In an open-set scenario, a recognition system should be capable of identifying known gesture classes while also handling unfamiliar gestures that appear during deployment. This paradigm enables models to adapt to new gesture categories with limited examples and improves their ability to function in dynamic and unconstrained environments. By focusing on the open-set learning framework, this work aims to make gesture recognition systems more flexible, scalable, and suitable for practical real-world applications where the set of possible gestures cannot always be predefined.
Keywords
Hand Gesture Recognition, Open-Set Learning, Viewpoint Variation, Joint-based Features, Incremental Learning
Citations
IRE Journals:
Prof. B. Venkateswarlu, M. Susmitha, R. Varalakshmi, S K. Muneer, T. Pavan "An Open-Set Framework for Robust Hand Gesture Classification" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 2152-2160 https://doi.org/10.64388/IREV9I9-1715462
IEEE:
Prof. B. Venkateswarlu, M. Susmitha, R. Varalakshmi, S K. Muneer, T. Pavan
"An Open-Set Framework for Robust Hand Gesture Classification" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715462
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