Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159528
Title: WiFi-vision enabled identification via multi-modal gait recognition
Authors: Deng, Lang
Keywords: Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Deng, L. (2022). WiFi-vision enabled identification via multi-modal gait recognition. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/159528
Abstract: This paper proposes GaitFi, a novel multi-modal gait recognition method, which uses WiFi signals and videos for human identification.
URI: https://hdl.handle.net/10356/159528
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: embargo_restricted_20240622
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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  Until 2024-06-22
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