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

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%.

Keywords

• convolutional-rnn
• gesture recognition
• hand gesture signatures
• in-air signatures