MaHG-RGBD: A Multi-angle View Hand Gesture RGB-D Dataset for Deep Learning Based Gesture Recognition and Baseline Evaluations

Here, we present a new dataset, named MaHG-RGBD, including 25 hand gestures performed by 15 participants as viewed from multiple angles. This dataset is intended to train models for deep-learning-based hand-gesture recognition. Unlike existing datasets, MaHG-RGBD includes not only front views (tilt angle = 0) but also the titled views (tilt angle = 45 degrees), which are often needed when there are space constraints. In addition, the dataset includes pairs of synchronized color and depth images of the hand region that are well segmented. Users can utilize just one of the image modalities or both depending on the application. This dataset includes a wide variety of different gestures classes: a total of 25 hand gestures. We evaluate the recognition accuracy of 25 different hand gestures using deep learning as a benchmark with this dataset. The MaHG-RGBD dataset is available at http://www.iipl.is.ritsumei.ac.jp/MaHG-RGBD.