# Smartphone Multi-modal Biometric Authentication: Database and Evaluation

arXiv: https://arxiv.org/abs/1912.02487 · HTML: https://mlsanity.com/paper/1912.02487/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Vulnerability of Automatic Identity Recognition to Audio-Visual Deepfakes](https://mlsanity.com/paper/2311.17655/) (2023)
- [Review of Face Presentation Attack Detection Competitions](https://mlsanity.com/paper/2112.11290/) (2021)
- [Finger-NestNet: Interpretable Fingerphoto Verification on Smartphone using Deep Nested Residual Network](https://mlsanity.com/paper/2212.05884/) (2022)
- [Multilingual Audio-Visual Smartphone Dataset And Evaluation](https://mlsanity.com/paper/2109.04138/) (2021)

## Abstract (arXiv)

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.