Depth Static Hand Gesture Recognition

Yao Mao Cheng*, Jyun Wei Huang, Chi Yu Sung, Bo Wei Li, Yu Chieh Chen, Shih Hung Yang, Yon Ping Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Finger-spelling recognition is the popular way to realize Human-Computer Interaction (HCI). The sign language datasets for finger-spelling recognition are mostly collected by the color camera and the depth camera. The depth camera directly captures the 3D information between the performer and lens, which can extract the precise hand gesture texture information without being influenced by the complex background and illumination. This work develops a hand gesture recognition system using depth image. This system employs a VGGNet to learn the feature extraction and classification. The experimental results show that the proposed hand gesture recognition system can correctly recognize the hand gesture using only depth image. This may reduce computational cost of the hand gesture recognition system.

Original languageEnglish
Title of host publication2021 International Automatic Control Conference, CACS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665444125
StatePublished - 2021
Event2021 International Automatic Control Conference, CACS 2021 - Chiayi, Taiwan
Duration: 3 Nov 20216 Nov 2021

Publication series

Name2021 International Automatic Control Conference, CACS 2021


Conference2021 International Automatic Control Conference, CACS 2021


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