Citation

Lim, Alvin Fang Chuen and Khoh, Wee How and Pang, Ying Han and Yap, Hui Yen (2024) Revolutionizing Signature Recognition: A Contactless Method with Convolutional Recurrent Neural Networks. International Journal of Technology, 15 (4). p. 1102. ISSN 2086-9614

Text IJTech_EECE-6744_Revolutionizing-Signature-Recognition-A-Contactles.pdf - Published Version Restricted to Repository staff only Download (770kB)

Abstract

Conventional contact-based hand signature recognition methods are raising hygienic concerns due to shared acquisition devices among the public. Therefore, this research aimed to propose a contactless in-air hand gesture signature (iHGS) recognition method using convolutional recurrent neural networks (C-RNN). Experiments have been conducted to identify the most suitable CNN architecture for the integration of CNN and RNN. A total of four base architectures were adopted and evaluated, namely MS-CNN-A, MS-CNN-B, CNN-A, and CNN-B. Based on the results, CNN-A was selected as the convolutional layer for constructing the integration of C-RNN due to its superior performance, achieving an accuracy rate of 95.15%. Furthermore, three variants of C-RNN were proposed, and experimental results on the iHGS database showed that the ConvBiLSTM achieved the highest accuracy at 98.10%, followed by ConvGRU at 97.47% and ConvLSTM at 97.40%.

Item Type:	Article
Uncontrolled Keywords:	Convolutional-RNN; Gesture recognition; Hand gesture signatures; In-air signatures
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Aug 2024 06:49
Last Modified:	01 Aug 2024 06:49
URII:	http://shdl.mmu.edu.my/id/eprint/12731

Downloads

Edit (login required)