Improving deep-learning-based face recognition to increase robustness against morphing attacks

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

13 Downloads (Pure)

Abstract

State-of-the-art face recognition systems (FRS) are vulnerable to morphing attacks, in which two photos of different people are merged in such a way that the resulting photo resembles both people. Such a photo could be used to apply for a passport, allowing both people to travel with the same identity document. Research has so far focussed on developing morphing detection methods. We suggest that it might instead be worthwhile to make face recognition systems themselves more robust to morphing attacks. We show that deep-learning-based face recognition can be improved simply by treating morphed images just like real images during training but also that, for significant improvements, more work is needed. Furthermore, we test the performance of our FRS on morphs of a type not seen during training. This addresses the problem of overfitting to the type of morphs used during training, which is often overlooked in current research.
Original languageEnglish
Title of host publication9th International Conference on Signal, Image Processing and Pattern Recognition (SPPR 2020), December 19 ~ 20, 2020, Sydney, Australia
EditorsDavid C. Wyld, Jan Zizka
PublisherAcademy and Industry Research Collaboration Center (AIRCC)
Number of pages12
DOIs
Publication statusPublished - 19 Dec 2020
Event9th International Conference on Signal, Image Processing and Pattern Recognition, SPPR 2020 - Sydney, Australia
Duration: 19 Dec 202020 Dec 2020
Conference number: 9

Publication series

NameComputer Science & Information Technology
PublisherAIRCC
Number19
Volume10
ISSN (Electronic)2231-5403

Conference

Conference9th International Conference on Signal, Image Processing and Pattern Recognition, SPPR 2020
Abbreviated titleSPPR
CountryAustralia
CitySydney
Period19/12/2020/12/20

Keywords

  • Biometrics
  • Morphing attack detection
  • Face Recognition
  • Vulnerability of Biometric Systems

Fingerprint Dive into the research topics of 'Improving deep-learning-based face recognition to increase robustness against morphing attacks'. Together they form a unique fingerprint.

Cite this