International Journal of Technology (Jul 2024)

Revolutionizing Signature Recognition: A Contactless Method with Convolutional Recurrent Neural Networks

  • Alvin Lim Fang Chuen,
  • Khoh Wee How,
  • Pang Ying Han,
  • Yap Hui Yen

DOI
https://doi.org/10.14716/ijtech.v15i4.6744
Journal volume & issue
Vol. 15, no. 4
pp. 1102 – 1117

Abstract

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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%.

Keywords