  Smartphones have been employed with biometric-based verification systems to
provide security in highly sensitive applications. Audio-visual biometrics are
getting popular due to their usability, and also it will be challenging to
spoof because of their multimodal nature. In this work, we present an
audio-visual smartphone dataset captured in five different recent smartphones.
This new dataset contains 103 subjects captured in three different sessions
considering the different real-world scenarios. Three different languages are
acquired in this dataset to include the problem of language dependency of the
speaker recognition systems. These unique characteristics of this dataset will
pave the way to implement novel state-of-the-art unimodal or audio-visual
speaker recognition systems. We also report the performance of the bench-marked
biometric verification systems on our dataset. The robustness of biometric
algorithms is evaluated towards multiple dependencies like signal noise,
device, language and presentation attacks like replay and synthesized signals
with extensive experiments. The obtained results raised many concerns about the
generalization properties of state-of-the-art biometrics methods in
smartphones.
