Dual Leap Motion Controller 2: A Robust Dataset for Multi-view Hand Pose Recognition

Abstract

Abstract This paper presents Multi-view Leap2 Hand Pose Dataset (ML2HP Dataset), a new dataset for hand pose recognition, captured using a multi-view recording setup with two Leap Motion Controller 2 devices. This dataset encompasses a diverse range of hand poses, recorded from different angles to ensure comprehensive coverage. The dataset includes real images with the associated precise and automatic hand properties, such as landmark coordinates, velocities, orientations, and finger widths. This dataset has been meticulously designed and curated to maintain a balance in terms of subjects, hand poses, and the usage of right or left hand, ensuring fairness and parity. The content includes 714,000 instances from 21 subjects of 17 different hand poses (including real images and 247 associated hand properties). The multi-view setup is necessary to mitigate hand occlusion phenomena, ensuring continuous tracking and pose estimation required in real human-computer interaction applications. This dataset contributes to advancing the field of multimodal hand pose recognition by providing a valuable resource for developing advanced artificial intelligence human computer interfaces.