Lip-based biometric authentication

This has made it increasingly difficult to tell where progress in the area has been made.<br><br>To try and address these issues this work investigates visual information from video input of the lips as a biometric for authentication. This work confirms that there is uniqueness that can be taken from the lips for biometric authentication, and sets a robust benchmark with a popular and widely available dataset using a Gaussian Mixture Model-Universal

Background

Model approach. New data was captured for the work in this thesis and used to investigate lip-based authentication under challenging real- world conditions. The results were analysed to provide a deeper understanding of the behaviours and shortcomings.

This work goes on to push the field forward through the application of deep learning. A deep artificial neural network is trained end-to-end, and one-shot-learning applied by implementing a Siamese network architecture. This approach achieved state-of-the-art performance for lip-based biometric authentication on the XM2VTS dataset.