10.21227/KCB8-PZ96
Yi Liu
Yi
Liu
Brunel University London
Mohammad R. Swash
Mohammad R.
Swash
Brunel University London
Hongying Meng
Hongying
Meng
0000-0002-8836-1382
Brunel University London
Holoscopic 3D Micro-Gesture Database
IEEE DataPort
2020
Artificial Intelligence
Image Processing
Computer Vision
holiscopic 3D imaging
gesture recognition
Machine Learning
2020-12-25
Open Access Dataset
Creative Commons Attribution
HoMG database was recorded using a holoscopic 3D camera, which have 3 conventional gestures from 40 participants under different settings and conditions. The principle of holoscopic 3D (H3D) imaging mimics fly’s eye technique that captures a true 3D optical model of the scene using a microlens array. For the purpose of H3D micro-gesture recognition. HoMG database has two subsets. The video subset has 960 videos and the image subset has 30635 images, while both have three type of microgestures (classes). Each subset has been devided into three partitions: training set, validation set and testing set where there is not overlap between them in term of the subjects. The database has been used for Holoscopic Micro-Gesture Recognition Challenge 2018 (HoMGR 2018) that was held at IEEE Face & Gesture 2018 (FG2018 ) - Xi'an, China, 15-19th May 2018 (https://fg2018.cse.sc.edu/Challenges.html). The database is now public available for wider research community in the research areas of holoscopic 3D image processing, machine learning for gesture recognition and its application in AR and VR.