  This paper proposes teeth-photo, a new biometric modality for human
authentication on mobile and hand held devices. Biometrics samples are acquired
using the camera mounted on mobile device with the help of a mobile application
having specific markers to register the teeth area. Region of interest (RoI) is
then extracted using the markers and the obtained sample is enhanced using
contrast limited adaptive histogram equalization (CLAHE) for better visual
clarity. We propose a deep learning architecture and novel regularization
scheme to obtain highly discriminative embedding for small size RoI. Proposed
custom loss function was able to achieve perfect classification for the tiny
RoI of $75\times 75$ size. The model is end-to-end and few-shot and therefore
is very efficient in terms of time and energy requirements. The system can be
used in many ways including device unlocking and secure authentication. To the
best of our understanding, this is the first work on teeth-photo based
authentication for mobile device. Experiments have been conducted on an
in-house teeth-photo database collected using our application. The database is
made publicly available. Results have shown that the proposed system has
perfect accuracy.
