Face authentication (FA) schemes are universally adopted. However, current FA systems are mainly camera-based and susceptible to masks and vulnerable to spoofing attacks. This paper exploits the penetrability, material sensitivity, and fine-grained sensing capability of millimeter wave (mmWave) to build an anti-spoofing FA system, named mmFace. It scans faces by moving a commodity mmWave radar along a specific trajectory. The signals bounced off the face carry facial biometric and structure features, which allows mmFace to achieve reliable liveness detection and FA. Due to the penetrability of mmWave, mmFace can still work well when users wear masks. To en- hance security, we develop a liveness detection method and an amplitude modulation-based method to defend against spoofing attacks and replay attacks. We enhance the basic version of mmFace [1] by improving its performance under mask occlusion and replay attack resilience. Besides, we explore a distance-resistant structure feature to suppress the impact of unstable face- to-device distance. To avoid on-site registration, we propose a novel virtual registration approach based on the cross-modal transformation from photos to mmWave. We implement mmFace with various antenna configurations and prototype two typical modes of mmFace. Extensive experiments demonstrate mmFace's accuracy in FA and effectiveness in attack detection.

Global Navigation Satellite Systems (GNSS) and base station (BS)-based wireless positioning are the predominant technologies for outdoor user equipment (UE) localization. However, their performance deteriorates significantly in dense urban environments with complex architectural structures. This degradation arises because obstructions disrupt line-of-sight (LoS) conditions between the UE and satellites or base stations, reducing positioning accuracy. To address this challenge, this paper introduces LocVMunet, a novel deep neural network designed for high-precision localization based on received signal strength (RSS) from a limited number of base stations. The proposed LocVMunet seamlessly integrates the Cross-Scan Module (CSM) with signal distribution modeling, enhancing spatial feature extraction and improving localization accuracy. Unlike traditional methods, this neural network-based approach inherently mitigates the impact of non-line-of-sight (NLOS) conditions, enabling real-time localization across any region covered by the radio map. Experimental evaluations demonstrate that the LocVMunet establishes a new benchmark for RSS-based urban localization, significantly outperforming state-of-the-art neural network-based methods. Notably, it achieves a 24.8% positioning error reduction over LocUNet and a 22.7% improvement over LocSwinUnet in the five-base-station setup, highlighting its robustness and effectiveness in urban positioning.

This paper presents the design and implementation of Fusang, a low-barrier system that brings accurate and robust 3D object recognition to Commercial-Off-The-Shelf mmWave devices. The basic idea of Fusang is leveraging the large bandwidth of mmWave Radars to capture a unique set of fine-grained reflected responses generated by object shapes. Moreover, Fusang constructs two novel graph-structured features to robustly represent the reflected responses of the signal in the frequency domain and IQ domain, and carefully designs a neural network to accurately recognize objects even in different multipath scenarios. We have implemented a prototype of Fusang on a commodity mmWave Radar device. Our experiments with 24 different objects show that Fusang achieves a mean accuracy of 97% in different multipath environments. The code, dataset, and trained models of Fusang can be obtained at https://github.com/OpenNISLab/Pro-Fusang.

Face authentication (FA) schemes are universally adopted. However, current FA systems are mainly camera-based and hence susceptible to face occlusion (e.g., facial masks) and vulnerable to spoofing attacks (e.g., 3D-printed masks). This paper exploits the penetrability, material sensitivity, and fine-grained sensing capability of millimeter wave (mmWave) to build an anti-spoofing FA system, named mmFace. It scans the human face by moving a commodity off-the-shelf (COTS) mmWave radar along a specific trajectory. The mmWave signals bounced off the human face carry the facial biometric features and structure features, which allows mmFace to achieve reliable liveness detection and FA. Due to the penetrability of mmWave, mmFace can still work well even if users wear masks. We explore a distance-resistant facial structure feature to suppress the impact of unstable face-to-device distance. To avoid inconvenient on-site registration, we also propose a novel virtual registration approach based on the core idea of cross-modal transformation from photos to mmWave signals. We implement mmFace with various antenna configurations and prototype two typical modes of mmFace. Extensive experiments show that mmFace can realize accurate FA as well as reliable liveness detection.