An Efficient Approach to De-Identifying Faces in Videos

Li Meng, Zongji Sun, Odette Tejada Collado

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
107 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)1039-1045
JournalIET Signal Processing
Volume11
Issue number9
Early online date27 Oct 2017
DOIs
Publication statusPublished - 1 Dec 2017

Keywords

  • DATA PRIVACY, FACE RECOGNITION, IDENTIFICATION, IMAGE PROCESSING, VIDEO SIGNALS

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