The development of smart homes has advanced the concept of user authentication to not only protecting user privacy but also facilitating personalized services to users. Along this direction, we propose to integrate user authentication with human-computer interactions between users and smart household appliances through widely-deployed WiFi infrastructures, which is non-intrusive and device-free. In this paper, we propose FingerPass which leverages Channel State Information (CSI) of surrounding WiFi signals to continuously authenticate users through finger gestures in smart homes. FingerPass separates the user authentication process into two stages, login and interaction, to achieve high authentication accuracy and low response latency simultaneously. In the login stage, we develop a deep learning-based approach to extract behavioral characteristics of finger gestures for highly accurate user identification. For the interaction stage, to provide continuous authentication in real time for satisfactory user experience, we design a verification mechanism with lightweight classifiers to continuously authenticate the user's identity during each interaction of finger gestures. Experiments in real environments show that FingerPass can achieve the authentication accuracies of 90.6% under in-domain scenarios and 87.6% under cross-domain scenarios, as well as 186.6ms response time during interactions.

Sensing technology plays a crucial role in bridging the physical and digital worlds. By transforming a multitude of physical phenomena into digital data, it significantly enhances our understanding of the environment and is instrumental in a wide range of applications. Given the wide bandwidth and short wavelength characteristics, millimeter wave (mmWave) radar sensing is considered one of the most promising sensing techniques beyond mmWave communication. In this paper, we provide a comprehensive survey of mmWave radar-based sensing techniques and applications in autonomous vehicles, smart homes, and industry. Specifically, we first review widely exploited mmWave radar techniques and signal processing techniques from the perspective of dedicated radars and communication integration, which are the basis of mmWave radar sensing. Then, we introduce mainstream machine learning techniques, especially the latest deep learning techniques for designing applications with mmWave signals. Related hardware devices, available public datasets, and evaluation metrics are also presented. Afterward, we provide a taxonomy of emerging mmWave radar sensing applications, and review the developments in object detection, ego-motion estimation, simultaneous localization and mapping, activity recognition, pose estimation, gesture recognition, speech recognition, vital sign monitoring, user authentication, indoor positioning, industrial imaging, industrial measurement, environmental monitoring, etc. We conclude the paper by discussing challenges and potential future research directions.

User authentication nowadays has become an important support for not only security guarantees but also emerging novel applications. Although WiFi signal-based user authentication has achieved initial success, it works in single-user scenarios while multi-user authentication remains a challenging task. In this paper, we present MultiAuth , a multi-user authentication system that can authenticate multiple users with a single pair of commodity WiFi devices. The basic idea is to profile multipath components of WiFi signals, and leverage the multipath components to characterize each user individually for multi-user authentication. MultiAuth first profiles multipath components of WiFi signals through a proposed MUltipath Time-of-Arrival estimation algorithm (MUTA). Then, after matching corresponding multipath components to each user in complex multi-user scenarios, MultiAuth constructs individual CSI based on the multipath components to characterize each user individually. An AoA-based approach is exploited to further separate individual CSI constructed by the users with same ToA. To identify users through their activities, MultiAuth extracts user behavior profiles based on the individual CSI, and leverages a dual-task neural network for robust user authentication. Extensive experiments involving 3 simultaneously present users demonstrate that MultiAuth is effective in multi-user authentication with 86.2% average accuracy and 9.5% average false accept rate.

Eavesdropping on human voice is one of the most common but harmful threats to personal privacy. Glasses are in direct contact with human face, which could sense facial motions when users speak, so human speech contents could be inferred by sensing the movements of glasses. In this paper, we present a live voice eavesdropping method, RF-Mic, which utilizes common glasses attached with a low-cost RFID tag to sense subtle facial speech dynamics for inferring possible voice contents. When a user with a glasses, which is attached an RFID tag on the glass bridge, is speaking, RF-Mic first collects RF signals through forward propagation and backscattering. Then, body motion interference is eliminated from the collected RF signals through a proposed Conditional Denoising AutoEncoder (CDAE) network. Next, RF-Mic extracts three kinds of facial speech dynamic features (i.e., facial movements, bone-borne vibrations, and airborne vibrations) by designing three different deep-learning models. Based on the extracted features, a facial speech dynamics model is constructed for live voice eavesdropping. Extensive experiments in different real environments demonstrate that RF-Mic can achieve robust and accurate human live voice eavesdropping.

The technology of facial expression reconstruction has paved the way for various face-centric applications such as virtual reality (VR) modeling, human-computer interaction, and affective computing. Existing vision-based solutions present challenges in privacy leakage and poor lighting conditions. In this paper, we introduce a nonintrusive facial expression reconstruction system, mm3DFace, which uses a millimeter wave (mmWave) radar to reconstruct facial expressions in a privacy-preserving and passive manner. mm3DFace first captures and pre-processes mmWave signals reflected by a human face, and extracts intricate facial geometric features using a ConvNeXt model integrated with triple loss embedding. Subsequently, mm3DFace derives pose-invariant facial representations utilizing region-divided affine transformation, and further generates individual facial shapes with 68 facial landmarks. Then, dynamic facial expressions with 3D facial avatars are reconstructed to exhibit realistic facial expressions. Finally, mm3DFace enables micro-expression recognition with mmWave signals, which ensures the capability of describing tiny facial changes. Through extensive real-world experiments involving 15 participants, mm3DFace achieves a normalized mean error of 3.94%, a mean absolute error of 2.30 mm, and a 3D-mean absolute error of 4.10 mm in tracking 68 facial landmarks, which demonstrates the efficacy and practicality of mm3DFace in real-world 3D facial reconstruction scenarios.