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This paper proposes teeth-photo, a new biometric modality for humanauthentication on mobile and hand held devices. Biometrics samples are acquiredusing the camera mounted on mobile device with the help of a mobile applicationhaving specific markers to register the teeth area. Region of interest (RoI) isthen extracted using the markers and the obtained sample is enhanced usingcontrast limited adaptive histogram equalization (CLAHE) for better visualclarity. We propose a deep learning architecture and novel regularizationscheme to obtain highly discriminative embedding for small size RoI. Proposedcustom loss function was able to achieve perfect classification for the tinyRoI of $75\times 75$ size. The model is end-to-end and few-shot and thereforeis very efficient in terms of time and energy requirements. The system can beused in many ways including device unlocking and secure authentication. To thebest of our understanding, this is the first work on teeth-photo basedauthentication for mobile device. Experiments have been conducted on anin-house teeth-photo database collected using our application. The database ismade publicly available. Results have shown that the proposed system hasperfect accuracy.

DeepTeeth: A Teeth-photo Based Human Authentication System for Mobile and Hand-held Devices