Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electrical and Electronic Engineering | en_US |
| dc.creator | Liu, Peidong | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13876 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Optimization of in-air hand gesture recognition system based on deep learning | en_US |
| dcterms.abstract | With the rapid development of the Internet, the number of personal devices owned by people is increasing, and many of them have authentication systems to protect personal privacy. In traditional authentication systems, the proliferation of accounts and devices may cause users to tend to use the same password or weak password variants. In addition, traditional recognition requires access to input devices, such as touch screens and keyboards. It will remain traces of use, increasing the probability that the password is cracked. Moreover, the user's biometric characteristics may remain and harm other interests. So. Non-contact recognition authentication is widely concerned, and gesture recognition is widely used in non-contact authentication. | en_US |
| dcterms.abstract | This work will be based on an in-air gesture user authentication system to complete the study of the principle and reproduce the gesture recognition function, which can realize the recognition of left and right hands, and the system can quickly recognize the current gesture by calling the camera. | en_US |
| dcterms.abstract | Furthermore, the optimisation efficiency of deep learning for gesture detection was investigated using the deep learning method. Based on the YOLOv5 algorithm, a new gesture recognition system is built, and real-time recognition can also retain an average accuracy of roughly 94.5%. | en_US |
| dcterms.extent | 39 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2023 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8282.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.6 MB | Adobe PDF | View/Open |
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