  Lip-based biometric authentication (LBBA) has attracted many researchers
during the last decade. The lip is specifically interesting for biometric
researchers because it is a twin biometric with the potential to function both
as a physiological and a behavioral trait. Although much valuable research was
conducted on LBBA, none of them considered the different emotions of the client
during the video acquisition step of LBBA, which can potentially affect the
client's facial expressions and speech tempo. We proposed a novel network
structure called WhisperNetV2, which extends our previously proposed network
called WhisperNet. Our proposed network leverages a deep Siamese structure with
triplet loss having three identical SlowFast networks as embedding networks.
The SlowFast network is an excellent candidate for our task since the fast
pathway extracts motion-related features (behavioral lip movements) with a high
frame rate and low channel capacity. The slow pathway extracts visual features
(physiological lip appearance) with a low frame rate and high channel capacity.
Using an open-set protocol, we trained our network using the CREMA-D dataset
and acquired an Equal Error Rate (EER) of 0.005 on the test set. Considering
that the acquired EER is less than most similar LBBA methods, our method can be
considered as a state-of-the-art LBBA method.
