WiDoor: Wi-Fi-Based Contactless Close-Range Identity Recognition

Abstract

In the fields of intelligent security and human-computer interaction, the rapid development of noncontact identity recognition technology based on Wi-Fi signals has shown promising application potential. To address the significant decrease in recognition accuracy in close-range scenarios, an close-range noncontact identity recognition method named WiDoor is proposed. During the data collection phase, the Fresnel propagation model is utilized by WiDoor to optimize the deployment layout of the receiving antennas. Gait information is reconstructed from the multiple antennas to enable the acquisition of more rich gait features. In the identity recognition stage, WiDoor employs a lightweight model that combines self-attention mechanisms with multiscale convolutional neural networks. This combination effectively enhances the model’s capability to capture key features while significantly reducing computational complexity and maintaining a high recognition accuracy. Experimental results show that WiDoor achieves a recognition accuracy of up to 99.3% on an expanded dataset that includes ten participants, with a distance of 1 m between the receiving and transmitting ends, and the parameter quantity of the built-in model is only 2% of the compared model with the same accuracy, offering a significant advantages over similar methods. Additionally, the model can achieve a high-precision recognition across different distances between the transmitter and the receiver using a limited number of samples, showing strong robustness of the model.