In-situ recognition of hand gesture via Enhanced Xception based single-stage deep convolutional neural network

作者：

Highlights：

• The Hybrid-SSR framework is proposed to perform real-time hand gesture recognition.

• The Enhanced Xception (E-Xception) architecture is utilized as a backbone network.

• Mitigates the misclassification problem encountered by the conventional models.

• The proposed model is evaluated on MITI-HD, NUSHP-II and Senz-3D datasets.

摘要

•The Hybrid-SSR framework is proposed to perform real-time hand gesture recognition.•The Enhanced Xception (E-Xception) architecture is utilized as a backbone network.•Mitigates the misclassification problem encountered by the conventional models.•The proposed model is evaluated on MITI-HD, NUSHP-II and Senz-3D datasets.

论文关键词：Hand gesture recognition,Convolutional neural network,Deep learning,Neural networks,Object detection

论文评审过程：Received 25 August 2021, Revised 8 November 2021, Accepted 18 December 2021, Available online 29 December 2021, Version of Record 5 January 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116427