Attribute-based Gesture Recognition: Generalization to Unseen Classes

In this paper, we present a hand pose based gesture representation that can effectively classify both static and dynamic gestures. In contrast to resource and data intensive DNN models, we postulate that hand pose alone can be used to extract compact yet discriminative features that are suitable for most applications that require real-time gesture recognition with minimal computational overhead. Building on the robustness of modern hand pose estimation frameworks and the expressive power of neural networks, we extract a fine-grained gesture description by decomposing gestures in a set of defined attributes. Our approach is highly capable of generalizing to unseen data and unseen classes, as shown by our experiments in the context of the BonnsAPPs Challenge use-case.