D4FLY Multimodal Biometric Database: multimodal fusion evaluation envisaging on-the-move biometric-based border control
Lulu Chen, Jonathan Boyle, Antonios Danelakis, James M. Ferryman, Simone Ferstl, Damjan Gicic, Artur Grudzień, Andre Howe, Marcin Kowalski, Krzysztof Mierzejewski, Theoharis Theoharis · 2021
This work presents a novel multimodal biometric dataset with emerging biometric traits including 3D face, thermal face, iris on-the-move, iris mobile, somatotype and smartphone sensors. This dataset was created to resemble on-the-move characteristics in applications such as border control. The five types of biometric traits were selected as they can be captured while on-the-move, are contactless, and show potential for use in a multimodal fusion verification system in a border control scenario. Innovative sensor hardware was used in the data capture. The data featuring these biometric traits will be a valuable contribution to advancing biometric fusion research in general. Baseline evaluation was performed on each unimodal dataset. Multimodal fusion was evaluated based on various scenarios for comparison. Real-time performance is presented based on an Automated Border Control (ABC) scenario.