Retaining Expression on De-identified Faces

Li Meng, Aruna Shenoy

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

12 Citations (Scopus)
27 Downloads (Pure)


The extensive use of video surveillance along with advances in face recognition has ignited concerns about the privacy of the people identifiable in the recorded documents. A face de-identification algorithm, named k-Same, has been proposed by prior research and guarantees to thwart face recognition software. However, like many previous attempts in face de-identification, kSame fails to preserve the utility such as gender and expression of the original data. To overcome this, a new algorithm is proposed here to preserve data utility as well as protect privacy. In terms of utility preservation, this new algorithm is capable of preserving not only the category of the facial expression (e.g., happy or sad) but also the intensity of the expression. This new algorithm for face de-identification possesses a great potential especially with real-world images and videos as each facial expression in real life is a continuous motion consisting of images of the same expression with various degrees of intensity.
Original languageEnglish
Title of host publicationSpeech and Computer
EditorsAlexey Karpov, Rodmonga Potapova, Iosif Mporas
PublisherSpringer Nature
ISBN (Electronic)978-3-319-66429-3
ISBN (Print)978-3-319-66428-6
Publication statusE-pub ahead of print - 13 Aug 2017
Event19th International Conference, SPECOM 2017 - Hatfield, United Kingdom
Duration: 12 Sept 201716 Sept 2017

Publication series

NameLecture Notes in Computer Science book series (LNCS, volume 10458)


Conference19th International Conference, SPECOM 2017
Country/TerritoryUnited Kingdom


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