FaceHash: Face Detection and Robust Hashing

Martin Steinebach; Huajian Liu; York Yannikos

Digital Forensics and Cyber Crime. Fifth International Conference, ICDF2C 2013, Moscow, Russia, September 26-27, 2013, Revised Selected Papers

Research Article

FaceHash: Face Detection and Robust Hashing

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@INPROCEEDINGS{10.1007/978-3-319-14289-0_8,
    author={Martin Steinebach and Huajian Liu and York Yannikos},
    title={FaceHash: Face Detection and Robust Hashing},
    proceedings={Digital Forensics and Cyber Crime. Fifth International Conference, ICDF2C 2013, Moscow, Russia, September 26-27, 2013, Revised Selected Papers},
    proceedings_a={ICDF2C},
    year={2015},
    month={2},
    keywords={Robust image hash Face detection Blob detection},
    doi={10.1007/978-3-319-14289-0_8}
}

Martin Steinebach
Huajian Liu
York Yannikos
Year: 2015
FaceHash: Face Detection and Robust Hashing
ICDF2C
Springer
DOI: 10.1007/978-3-319-14289-0_8

Martin Steinebach¹^,*, Huajian Liu¹, York Yannikos¹

1: Fraunhofer SIT

*Contact email: martin.steinebach@sit.fraunhofer.de

Abstract

In this paper, we introduce a concept to counter the current weakness of robust hashing with respect to cropping. We combine face detectors and robust hashing. By doing so, the detected faces become a subarea of the overall image which always can be found as long as cropping of the image does not remove the faces. As the face detection is prone to a drift effect altering size and position of the detected face, further mechanisms are needed for robust hashing. We show how face segmentation utilizing blob algorithms can be used to implement a face-based cropping robust hash algorithm.

Keywords: Robust image hash Face detection Blob detection

Published: 2015-02-06
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-319-14289-0_8