One-Shot-Learning for Visual Lip-Based Biometric Authentication

摘要

Lip-based biometric authentication is the process of verifying an individual's identity based on visual information taken from hps 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.