Automatic Generation and Detection of Visually Faultless Facial Morphs

Andrey Makrushin, Tom Neubert, Jana Dittmann

Abstract

This paper introduces an approach to automatic generation of visually faultless facial morphs along with a proposal on how such morphs can be automatically detected. It is endeavored that the created morphs cannot be recognized as such with the naked eye and a reference automatic face recognition (AFR) system produces high similarity scores while matching a morph against faces of persons who participated in morphing. Automatic generation of morphs allows for creating abundant experimental data, which is essential (i) for evaluating the performance of AFR systems to reject morphs and (ii) for training forensic systems to detect morphs. Our first experiment shows that human performance to distinguish between morphed and genuine face images is close to random guessing. In our second experiment, the reference AFR system has verified 11.78% of morphs against any of genuine images at the decision threshold of 1% false acceptance rate. These results indicate that facial morphing is a serious threat to access control systems aided by AFR and establish the need for morph detection approaches. Our third experiment shows that the distribution of Benford features extracted from quantized DCT coefficients of JPEG-compressed morphs is substantially different from that of genuine images enabling the automatic detection of morphs.

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Paper Citation


in Harvard Style

Makrushin A., Neubert T. and Dittmann J. (2017). Automatic Generation and Detection of Visually Faultless Facial Morphs . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 39-50. DOI: 10.5220/0006131100390050


in Bibtex Style

@conference{visapp17,
author={Andrey Makrushin and Tom Neubert and Jana Dittmann},
title={Automatic Generation and Detection of Visually Faultless Facial Morphs},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={39-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006131100390050},
isbn={978-989-758-227-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Automatic Generation and Detection of Visually Faultless Facial Morphs
SN - 978-989-758-227-1
AU - Makrushin A.
AU - Neubert T.
AU - Dittmann J.
PY - 2017
SP - 39
EP - 50
DO - 10.5220/0006131100390050