UltraFace: Secure User-friendly Facial Authentication on Smartphones Using Ultrasound

With a wide range of common and privacy-sensitive applications, smartphones are frequently accessed for substantial personal information. Therefore, user-friendliness and security are crucial for user authentication on smartphones. Recently, convenient and secure biometric-based authentication is widely employed for smartphones, where the facial authentication stands out due to its potential for advancements in both user-friendliness and security. However, existing facial authentication methods possess some defects. For example, camera-based methods require good illumination conditions and are susceptible to 2D spoofing attacks. Moreover, previous acoustic-based methods either require camera assistance, or still suffer from 3D spoofing attacks. Even worse, some acoustic-based methods use audible sound waves, causing discomfort to users. To solve these questions, in this paper we propose UltraFace, an anti-spoofing and user-friendly facial authentication system on smartphones. It extracts facial geometry features and acoustic impedance features from imperceptible ultrasound. Leveraging the principle of ultrasound propagation, UltraFace correlates spectrograms of reflected signals with facial biometrics. Utilizing a deep learning model as a feature extractor, UltraFace mines fine-grained facial geometry features and acoustic impedance features from the spectrograms for accurate user authentication. Extensive experiments show that UltraFace achieves $97.2 \%$ accuracy in user authentication and can effectively defend against spoofing attacks. Furthermore, UltraFace exhibits robustness for long-term usage.