Dynamic face authentication systems: Deep learning verification for camera close-Up and head rotation paradigms

Abstract

The popularity of face-authentication systems has also generated interest in the study of malicious authentication attempts, such as face spoofing attacks. In this study we investigate two dynamic face-authentication challenges: the camera close-up, and head-rotation paradigms. For each paradigm we developed an ML-based face-authentication system that performs the tasks of liveness detection and face verification. In order to generate structured data-representations from the videos collected in the wild, we designed feature representations that extract three-dimensional and spatial characteristics of a face, while also capturing the particular liveness cues of the requested challenge-based movements. Furthermore, a set of Neural-Network models that employ Convolutional Neural Networks and Siamese Neural Network architectures were proposed. To train and test our models we collected a dataset of 177 live videos recorded by 41 different subjects and a set of 243 attack attempts in uncontrolled scenarios. The resulting NN models yield good performance against multiple types of media-based attacks (printed-attacks, screen-attacks, 2D-masks, videos acquired from public social media, deep fakes). The camera close-up system presented an overall liveness detection accuracy of 97.7% and a face verification accuracy of 97.6%. On the other hand, the evaluation of the head-rotation system resulted in a liveness detection accuracy of 92.4% and face verification accuracy of 98.1%. Face authentication methods based on dynamic user-challenges constitute a scalable approach that does not require specialized hardware to increase the security of face authentication systems in realistic usage scenarios. The proposed methods not only make it harder for attackers to generate spoofing attacks, but they also constitute a practical complement to static biometric authentication systems.