  Biometric-based verification is widely employed on the smartphones for
various applications, including financial transactions. In this work, we
present a new multimodal biometric dataset (face, voice, and periocular)
acquired using a smartphone. The new dataset is comprised of 150 subjects that
are captured in six different sessions reflecting real-life scenarios of
smartphone assisted authentication. One of the unique features of this dataset
is that it is collected in four different geographic locations representing a
diverse population and ethnicity. Additionally, we also present a multimodal
Presentation Attack (PA) or spoofing dataset using a low-cost Presentation
Attack Instrument (PAI) such as print and electronic display attacks. The novel
acquisition protocols and the diversity of the data subjects collected from
different geographic locations will allow developing a novel algorithm for
either unimodal or multimodal biometrics. Further, we also report the
performance evaluation of the baseline biometric verification and Presentation
Attack Detection (PAD) on the newly collected dataset.
