Accessibility navigation


D4FLY multimodal biometric database: multimodal fusion evaluation envisaging on-the-move biometric-based border control

Chen, L., Boyle, J., Danelakis, A., Ferryman, J., Ferstl, S., Gicic, D., Grudzien, A., Howe, A., Kowalski, M., Mierzejewski, K. and Theoharis, T. (2021) D4FLY multimodal biometric database: multimodal fusion evaluation envisaging on-the-move biometric-based border control. In: 17th IEEE Int'l Conf on Advanced Video and Signal-based Surveillance (AVSS 2021), November 16-19, 2021, Virtual. (In Press)

[img] Text - Accepted Version
· Restricted to Repository staff only
· The Copyright of this document has not been checked yet. This may affect its availability.

2MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Abstract/Summary

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.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:101891

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation