Attention based residual network for micro-gesture recognition

doi:10.1109/FG.2018.00127

	Attention based residual network for micro-gesture recognition
	Peng, Min1 ; Wang, Chongyang 2; Chen, Tong 3
	2018
会议日期	May 15, 2018 - May 19, 2018
会议地点	Xi'an, China
DOI	10.1109/FG.2018.00127
页码	790-794
英文摘要	Finger micro-gesture recognition is increasingly become an important part of human-computer interaction (HCI) in applications of augmented reality (AR) and virtual reality (VR) technologies. To push the boundary of microgesture recognition, a novel Holoscopic 3D Micro-Gesture Database (HoMG) was established for research purpose. HoMG has an image subset and a video subset. This paper is to demonstrate the result achieved on the image subset for Holoscopic Micro-Gesture Recognition Challenge 2018 (HoMGR 2018). The proposed method utilized the state-of-the-art residual network with an attention-involved design. In every block of the network, an attention branch is added to the output of the last convolution layer. The attention branch is designed to spotlight the finger micro-gesture and reduce the noise introduced from the wrist and background. With an extensive analysis on HoMG, the proposed model achieved a recognition accuracy of 80.5% on the validation set and 82.1% on the testing set. © 2018 IEEE.
会议录	13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
语种	英语
内容类型	会议论文
源URL	[http://119.78.100.138/handle/2HOD01W0/7969]
专题	中国科学院重庆绿色智能技术研究院
作者单位	1.Intelligent Media Technique Research Center, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China; 2.UCL Interaction Centre, University College London, London, United Kingdom; 3.College of Electronic and Information Engineering, Southwest University, Chongqing, China
推荐引用方式 GB/T 7714	Peng, Min,Wang, Chongyang,Chen, Tong. Attention based residual network for micro-gesture recognition[C]. 见:. Xi'an, China. May 15, 2018 - May 19, 2018.