Continuous biometric authentication using Possibilistic C-Means
Matheus Santos, Maurício Pamplona Segundo · 2018
We propose a continuous biometric authentication framework that uses the Possibilistic C-Means (PCM) algorithm to guarantee that only authorized users can access a protected system. PCM is employed to cluster a history of biometric samples in two classes: genuine and impostor. The degree of membership of the current biometric sample to those classes is then used as a score, which is fused over time to reach a decision regarding the safety of the system. The main advantage of our approach is that it is training-free, and thus is applicable to any biometric feature that can be captured continuously without modification. We evaluated our system using 2D, 3D and NIR videos of faces and achieved results comparable to a training-based state-of-art work.