@inproceedings{de635120be8e44cd9bd5b87e3bae8d30,
title = "One-Shot-Learning for Visual Lip-Based Biometric Authentication",
abstract = "Lip-based biometric authentication is the process of verifying an individual{\textquoteright}s identity based on visual information taken from lips whilst speaking. To date research in this area has involved more traditional approaches and inconsistent results that are difficult to compare. This work aims to push the field forward through the application of deep learning. A deep artificial neural network using spatiotemporal convolutional and bidirectional gated recurrent unit layers is trained end-to-end. For the first time one-shot-learning is applied to lip-based biometric authentication by implementing a siamese network architecture, meaning the model only needs a single prior example in order to authenticate new users. This approach sets a new state-of-the-art performance for lip-based biometric authentication on the XM2VTS dataset and Lausanne protocol with an equal error rate of 0.93% on the evaluation set and a false acceptance rate of 1.07% at a 1% false rejection rate.",
keywords = "Biometric authentication, Lip-based, One-shot-learning, Siamese network, XM2VTS",
author = "Carrie Wright and Darryl Stewart",
year = "2019",
month = oct,
day = "21",
doi = "10.1007/978-3-030-33720-9_31",
language = "English",
isbn = "9783030337193",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "405--417",
editor = "George Bebis and Bahram Parvin and Richard Boyle and Darko Koracin and Daniela Ushizima and Sek Chai and Shinjiro Sueda and Xin Lin and Aidong Lu and Daniel Thalmann and Chaoli Wang and Panpan Xu",
booktitle = "Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings",
note = "14th International Symposium on Visual Computing, ISVC 2019 ; Conference date: 07-10-2019 Through 09-10-2019",
}