University of Hertfordshire

From the same journal

By the same authors

An Efficient Approach to De-Identifying Faces in Videos

Research output: Contribution to journalArticle

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  • 08221684

    Final published version, 4 MB, PDF document

  • Li Meng
  • Zongji Sun
  • Odette Tejada Collado
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Original languageEnglish
Pages (from-to)1039-1045
JournalIET Signal Processing
Journal publication date1 Dec 2017
Volume11
Issue9
Early online date27 Oct 2017
DOIs
Publication statusPublished - 1 Dec 2017

Abstract

This study presents a novel approach that extends face de-identification from person-specific (closed) sets of facial images to open sets of video frames. Inspired by the previous work in facial expression transfer, the authors have introduced an `identity shift' to ensure identity consistency within a de-identified video sequence. The `identity shift' is derived from the first video frame of a person and is then applied in the de-identification of all subsequent frames of the same person. Experimental results show that video frames that are originally associated with the same person will remain related to a common new identity after the application of the proposed approach. In addition, the proposed approach is able to achieve privacy protection as well as preservation of dynamic facial expressions. Finally, MATLAB implementation of the approach has confirmed its potential to operate in real time at the highest standard video frame rate.

Notes

This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/3.0/).

ID: 12446938