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.

Wi-Fi-based contactless activity recognition is of great importance to computer–human interaction, accounting for convenience concerns. However, it remains challenging to recognize activities from multiple users due to the multipath distortion and disruption of Wi-Fi signals. In this article, we propose a highly universal framework, namely, WISDOM, for Wi-Fi-based multiuser activity recognition. Specifically, we first leverage an existing model to identify the number of users from the input Wi-Fi signals. Then, we develop a subcarrier correlation and inversion-based sorting algorithm to extract the signal for each user. Finally, we design a neural network, i.e., WISDOM-Net, which is built on a bidirectional gated recurrent unit network incorporated with the attention mechanism and the one dimension convolutional neural network, to recognize the corresponding user activities. Experimental results show that our proposed WISDOM-Net outperforms the existing baselines on both the public and our own data sets. In particular, WISDOM-Net can reach an average recognition accuracy of up to 98.19% and 90.77% in 2-user and 3-user scenarios, respectively.

WiFi-based identity recognition is predominant because of its noninvasive and ubiquitous advantages. However, existing approaches show slow training speed and limited applicability. In this article, we propose a lightweight deep learning model, named as lightweight WiFi-based identification (LW-WiID), to address these technical challenges. LW-WiID reconstructs original data of channel state information into frequency energy graph, which contains not only the temporal feature of the gait but also the spatial feature among subcarriers, ensuring the accuracy of identity recognition. Furthermore, a novel Balloon mechanism is designed to achieve the lightweight. Through information integration crossing both layers and channels, the Balloon mechanism effectively reduces the number of model parameters. Experimental results demonstrate that LW-WiID achieves an accuracy of 99.7% on a 50-person gait data set while the model size is compressed to 5.53% of the existing identity recognition approaches with the same accuracy.