Face Presentation Attack Detection using Biologically-inspired Features

Aristeidis Tsitiridis, Cristina Conde, Isaac Martín De Diego, Enrique Cabello

2017

Abstract

A person intentionally concealing or faking their identity from biometric security systems is known to perform a ‘presentation attack’. Efficient presentation attack detection poses a challenging problem in modern biometric security systems. Sophisticated presentation attacks may successfully spoof a person’s face and therefore, disrupt accurate biometric authentication in controlled areas. In this work, a presentation attack detection technique which processes biologically-inspired facial features is introduced. The main goal of the proposed method is to provide an alternative foundation for biometric detection systems. In addition, such a system can be used for future generation biometric systems capable of carrying out rapid facial perception tasks in complex and dynamic situations. The newly-developed model was tested against two different databases and classifiers. Presentation attack detection results have shown promise, exceeding 94% detection accuracy on average for the investigated databases. The proposed model can be enriched with future enhancements that can further improve its effectiveness and complexity in more diverse situations and sophisticated attacks in the real world.

Paper Citation

in Harvard Style

Tsitiridis A., Conde C., Martín De Diego I. and Cabello E. (2017). Face Presentation Attack Detection using Biologically-inspired Features . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 360-370. DOI: 10.5220/0006124603600370

in Bibtex Style

@conference{visapp17,
author={Aristeidis Tsitiridis and Cristina Conde and Isaac Martín De Diego and Enrique Cabello},
title={Face Presentation Attack Detection using Biologically-inspired Features},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={360-370},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006124603600370},
isbn={978-989-758-225-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Face Presentation Attack Detection using Biologically-inspired Features
SN - 978-989-758-225-7
AU - Tsitiridis A.
AU - Conde C.
AU - Martín De Diego I.
AU - Cabello E.
PY - 2017
SP - 360
EP - 370
DO - 10.5220/0006124603600370