Hand gesture recognition via deep data optimization and 3D reconstruction.

ABSTRACT: Hand gesture recognition (HGR) are the most significant tasks for communicating with the real-world environment. Recently, gesture recognition has been extensively utilized in diverse domains, including but not limited to virtual reality, augmented reality, health diagnosis, and robot interaction. On the other hand, accurate techniques typically utilize various modalities generated from RGB input sequences, such as optical flow which acquires the motion data in the images and videos. However, this approach impacts real-time performance due to its demand of substantial computational resources. This study aims to introduce a robust and effective approach to hand gesture recognition. We utilize two publicly available benchmark datasets. Initially, we performed preprocessing steps, including d

SUBMITTER: Mustafa Z 

PROVIDER: S-EPMC10703008 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature