One-shot lip-based biometric authentication: extending behavioral features with authentication phrase information
Brando Koch, Ratko Grbi\'c

TL;DR
This paper enhances lip-based biometric authentication by incorporating speech content recognition to prevent replay attacks, using a customized dataset and a siamese neural network with triplet loss, achieving low error rates.
Contribution
It introduces a novel one-shot LBBA method that models speech content alongside behavioral features, improving security against replay attacks.
Findings
Achieved 3.2% FAR and 3.8% FRR on customized dataset.
Demonstrated the effectiveness of speech content modeling in LBBA.
Quantified the importance of behavioral versus physical features.
Abstract
Lip-based biometric authentication (LBBA) is an authentication method based on a person's lip movements during speech in the form of video data captured by a camera sensor. LBBA can utilize both physical and behavioral characteristics of lip movements without requiring any additional sensory equipment apart from an RGB camera. State-of-the-art (SOTA) approaches use one-shot learning to train deep siamese neural networks which produce an embedding vector out of these features. Embeddings are further used to compute the similarity between an enrolled user and a user being authenticated. A flaw of these approaches is that they model behavioral features as style-of-speech without relation to what is being said. This makes the system vulnerable to video replay attacks of the client speaking any phrase. To solve this problem we propose a one-shot approach which models behavioral features to…
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Taxonomy
TopicsSpeech and Audio Processing · Face recognition and analysis · Infant Health and Development
MethodsTriplet Loss
