Abstract
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.
Highlights
In today’s society, mobile devices such as phones, ts and laptops are considered essential for both personal and business purposes and the risks of passwords as a sole means of authentication is widely recognised
The aim of the work in this paper is to rigorously test LipAuth, a Lip-Based Biometric Authentication (LBBA) solution that uses one-shot-learning, with real-world data to better understand its suitability for mobile devices
The experiments include comparison closed-set protocol to the new open-set protocol, benchmarking real-world datasets, varied enrolment and authentication data, and a range of potential challenging situations that would be possible with LBBA on a mobile device
Summary
In today’s society, mobile devices such as phones, ts and laptops are considered essential for both personal and business purposes and the risks of passwords as a sole means of authentication is widely recognised. These devices can provide a gateway to gaining access to private and confidential data and online services such as social media, financial services and ecommerce services. Biometric traits can be physiological or behavioural Physiological biometrics such as face or fingerprint have already been successfully rolled out in many state-of-theart devices, both of these examples have been spoofed in high profile media cases. If liveness is successfully incorporated within a biometric system it could prevent face recognition systems from being spoofed using photographs or artificial
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
