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Any spatio-temporal movement or reorientation of the hand, done with the intention of conveying a specific meaning, can be considered as a hand gesture. Inputs to hand gesture recognition systems can be in several forms, such as depth images, monocular RGB, or skeleton joint points. We observe that raw depth images possess low contrasts in the hand regions of interest (ROI). They do not highlight important details to learn, such as finger bending information (whether a finger is overlapping the palm, or another finger). Recently, in deep-learning--based dynamic hand gesture recognition, researchers are tying to fuse different input modalities (e.g. RGB or depth images and hand skeleton joint points) to improve the recognition accuracy. In this paper, we focus on dynamic hand gesture (DHG) recognition using depth quantized image features and hand skeleton joint points. In particular, we explore the effect of using depth-quantized features in Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based multi-modal fusion networks. We find that our method improves existing results on the SHREC-DHG-14 dataset. Furthermore, using our method, we show that it is possible to reduce the resolution of the input images by more than four times and still obtain comparable or better accuracy to that of the resolutions used in previous methods.
Deep learning approaches deliver state-of-the-art performance in recognition of spatiotemporal human motion data. However, one of the main challenges in these recognition tasks is limited available training data. Insufficient training data results in
The purpose of gesture recognition is to recognize meaningful movements of human bodies, and gesture recognition is an important issue in computer vision. In this paper, we present a multimodal gesture recognition method based on 3D densely convoluti
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Gesture recognition and hand motion tracking are important tasks in advanced gesture based interaction systems. In this paper, we propose to apply a sliding windows filtering approach to sample the incoming streams of data from data gloves and a deci
Gesture recognition is a fundamental tool to enable novel interaction paradigms in a variety of application scenarios like Mixed Reality environments, touchless public kiosks, entertainment systems, and more. Recognition of hand gestures can be nowad