DTeeth: Teeth-photo Based Human Authentication for Mobile Devices

Abstract

This paper investigates teeth-photo, a new biometric modality, for human authentication on mobile and hand-held devices. The proposed system is suitable for multiple applications including device unlocking and secure authentication. Teeth samples have been acquired using a mobile application having markers to register the teeth area. The region of interest (RoI) is then extracted using the markers and the same is enhanced for better visual clarity. A deep learning architecture along with the feature regularization scheme is devised to obtain highly discriminative embedding. The model is trained in an end-to-end manner with a few samples and thus, is efficient in terms of time and energy requirements. Experiments have been conducted on an in-house teeth-photo database collected using the proposed application from 92 subjects each providing 10 samples in multiple sessions over a span of 3–4 days. It has been observed that the proposed system achieved 97.61% accuracy with a Correct Recognition Rate (CRR) of 95% at an Equal Error Rate (EER) as low as 2.07% even for a small RoI of size 175 × 175. To the best of our understanding, this is the first work on teeth-photo-based authentication for mobile devices. The database along with the code is being made public.