With the rapid development of augmented reality (AR) and virtual reality (VR) technology, human-computer interaction (HCI) has been greatly improved for gaming interaction of AR and VR control. The finger micro-gesture is one of the important interactive methods for HCI applications such as in the Google Soli and Microsoft Kinect projects. However, the progress in this research is slow due to the lack of high quality public available database. In this paper, holoscopic 3D camera is used to capture high quality micro-gesture images and a new unique holoscopic 3D micro-gesture (HoMG) database is produced. The principle of the holoscopic 3D camera is based on the fly viewing system to see the objects. HoMG database recorded the image sequence of 3 conventional gestures from 40 participants under different settings and conditions. For the purpose of micro-gesture recognition, HoMG has a video subset with 960 videos and a still image subset with 30635 images. Initial micro-gesture recognition on both subsets has been conducted using traditional 2D image and video features and popular classifiers and some encouraging performance has been achieved. The database will be available for the research communities and speed up the research in this area.
@cite_15 presents a review summary of 21 gesture datasets from previous research and publicly datasets, in which 7 databases are for hands. Most datasets are recorded using to the Kinect or RGB-D camera as the sensor.
@cite_15
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Abstract-People communicate through words and gestures,but current voice-based computer interfaces such as Siri exploitonly words. This is a shame: human-computer interfaces wouldbe natural if they incorporated gestures as well as words. Tosupport this goal, we present a new dataset of naturally occurringgestures made by people working collaboratively on blocks worldtasks. The dataset, called EGGNOG, contains over 8 hours ofRGB video, depth video, and Kinect v2 body position data of 40subjects. The data has been semi-automatically segmented into24,503 movements, each of which has been labeled accordingto (1) its physical motion and (2) the intent of the participant.We believe this dataset will stimulate research into natural andgestural human-computer interfaces