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

Smartphone Multi-modal Biometric Authentication: Database and Evaluation