Deep Person Identification Using Spatiotemporal Facial Motion Amplification
K. Gkentsidis, T. Pistola, Nikolaos Mitianoudis, Nikolaos V. Boulgouris · 2020
We explore the capabilities of a new biometric trait, which is based on information extracted through facial motion amplification. Unlike traditional facial biometric traits, the new biometric does not require the visibility of facial features, such as the eyes or nose, that are critical in common facial biometric algorithms. In this paper we propose the formation of a spatiotemporal facial blood flow map, constructed using small motion amplification. Experiments show that the proposed approach provides significant discriminatory capacity over different training and testing days and can be potentially used in situations where traditional facial biometrics may not be applicable.