SHREC 2021: Skeleton-based hand gesture recognition in the wild. (October 2021)

• Record Type: Journal Article
• Authors: Caputo, Ariel; Giachetti, Andrea; Soso, Simone; Pintani, Deborah; D'Eusanio, Andrea; Pini, Stefano; Borghi, Guido; Simoni, Alessandro; Vezzani, Roberto; Cucchiara, Rita; Ranieri, Andrea; Giannini, Franca; Lupinetti, Katia; Monti, Marina; Maghoumi, Mehran; LaViola Jr, Joseph J.; Le, Minh-Quan; Nguyen, Hai-Dang; Tran, Minh-Triet
• Is Part Of: Computers & graphics. Volume 99(2021)
• Page Start: 201
• Page End: 211
• Publication Date: 2021-10
• Subjects: Gesture recognition -- Hand skeleton -- Online -- Interaction; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2021.07.007
• Languages: English
• ISSNs: 0097-8493

Abstract

Highlights: 3D Shape Retrieval Challenge 2021 at 3DOR'21 Track on Skeleton-based Hand Gesture Recognition in the Wild. New gesture dataset with 180 gestures sequences and 18 gestures dictionary. Contest with 4 groups presenting their gesture recognition methods. Report of results and performances for all the methods. Graphical abstract: Abstract: 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 nowadays performed directly from the stream of hand skeletons estimated by software provided by low-cost trackers (Ultraleap) and MR headsets (Hololens, Oculus Quest) or by video processing software modules (e.g. Google Mediapipe). Despite the recent advancements in gesture and action recognition from skeletons, it is unclear how well the current state-of-the-art techniques can perform in a real-world scenario for the recognition of a wide set of heterogeneous gestures, as many benchmarks do not test online recognition and use limited dictionaries. This motivated the proposal of the SHREC 2021: Track on Skeleton-based Hand Gesture Recognition in the Wild. For this contest, we created a novel dataset with heterogeneous gestures featuring different types and duration. These gestures have to be found inside sequences in an online recognition scenario. This paper presents the result of the contest, showing the performances of the techniques proposed by four research groups on the challenging task compared with a simple baseline method.