Citation

Lim, Alvin Fang Chuen and Khoh, Wee How and Pang, Ying Han and Yap, Hui Yen (2023) In-Air Hand Gesture Signature Recognition Using Multi-Scale Convolutional Neural Networks. JOIV : International Journal on Informatics Visualization, 7 (3-2). p. 2025. ISSN 2549-9610

Text 41.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

The hand signature is a unique handwritten name or symbol that serves as a proof of identity. Due to its practicality and widespread use, hand signature is still used by financial institutions as a means of verifying and validating the identity of their customers. The emergence of the COVID-19 global pandemic has raised hygiene concerns regarding the conventional touch-based hand signature recognition system, which often requires sharing the acquisition devices among the public. This paper presents in-air hand gesture signature recognition using convolutional neural networks to address this concern. We designed a shallow multi-scale convolutional neural network using 3x3 and 5x5 kernel filter sizes to extract features on different scales. The feature maps from these two filters are then concatenated to provide more robust features, which improve the model’s performance. The proposed architecture was evaluated on the In-Air Hand Gesture Database (iHGS) and compared its performance with other existing architectures, including GoogleNet, AlexNet, VGG-16, and ResNet-50, under the same experimental setting. The experiment results show that the proposed architecture outperforms other architectures, which obtained the highest accuracy of 93.00%. On the other hand, our architecture consumed significantly fewer computational resources, requiring only an average of 3 minutes and 33 seconds to train. Additionally, the performance of the proposed architecture could be further enhanced by integrating it with recurrent neural networks (RNN). This integrated architecture of convolutional recurrent neural networks (C-RNN) can capture spatio-temporal features simultaneously

Item Type:	Article
Uncontrolled Keywords:	Hand gesture signature; gesture recognition; in-air signatures; convolutional neural networks.
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	27 Mar 2024 02:16
Last Modified:	27 Mar 2024 02:16
URII:	http://shdl.mmu.edu.my/id/eprint/12200

Downloads

Edit (login required)