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This paper explores the suitability of lip-based authentication as a behavioural biometric for mobile devices. Lip-based biometric authentication is the process of verifying an individual based on visual information taken from the lips while speaking. It is particularly suited to mobile devices because it contains unique information; its potential for liveness over existing popular biometrics such as face and fingerprint and lip movements can be captured using a device's front-facing camera, requiring no dedicated hardware. Despite its potential, research and progress into lip-based biometric authentication has been significantly slower than other biometrics such as face, fingerprints, or iris. This paper investigates a state-of-the-art approach using a deep Siamese network, trained with the triplet loss for one-shot lip-based biometric authentication with real-world challenges. The proposed system, LipAuth, is rigourously examined with real-world data and challenges that could be expected on lip-based solution deployed on a mobile device. The work in this paper shows for the first time how a lip-based authentication system performs beyond a closed-set protocol, benchmarking a new open-set protocol with an equal error rates of 1.65% on the XM2VTS dataset. New datasets, qFace and FAVLIPS, were collected for the work in this paper, which push the field forward by enabling systematic testing of the content and quantities of data needed for lip-based biometric authentication and highlight problematic areas for future work. The FAVLIPS dataset was designed to mimic some of the hardest challenges that could be expected in a deployment scenario and include varied spoken content, miming and a wide range of challenging lighting conditions. The datasets captured for this work are available to other university research groups on request.

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