Fusing shape and spatio-temporal features for depth-based dynamic hand gesture recognition

Hand gesture recognition has many practical applications including human-computer interfaces. Many depth-based features for dynamic hand gesture recognition task have been proposed. However the performance is still unsatisfactory due to the limitation that these features can’t efficiently capture both effective shape information and detailed variation of hands in spatial and temporal domains. In this paper, we propose a new effective descriptor, DLEH2, for depth-based dynamic hand gesture recognition which is developed based on the characteristics of dynamic hand gesture through fusing simple shape and spatio-temporal features of depth sequences. For shape information, depth motion maps (DMMs) are first employed to obtain 3D structure and shape information of hands. To enhance critical shape cues, the local texture and edge information of three DMMs for hand gesture sequences are captured using DLE descriptor. However, DMMs compress the temporal information of the depth sequences into space domain, which loses critical discrimination for temporal sequence recognition to some degree. Simple but effective spatio-temporal features, HOG2, are concatenated with DLE to compensate the temporal information loss during DMMs generation and capture the detailed spatial and temporal variation of hands. Experimental results on two public benchmark datasets, 99.10 % for MSRGesture3D dataset and 98.43 % for SKIG dataset, show that the proposed fusion scheme outperforms the state-of-the-art methods.