Logo des Repositoriums
 

Face Image De-identification Based on Feature Embedding for Privacy Protection

Kurzanzeige
dc.contributor.authorGoki Hanawa, Koichi Ito
dc.contributor.editorDamer, Naser
dc.contributor.editorGomez-Barrero, Marta
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorSequeira, Ana F.
dc.contributor.editorTodisco, Massimiliano
dc.contributor.editorUhl, Andreas
dc.date.accessioned2023-12-12T10:46:46Z
dc.date.available2023-12-12T10:46:46Z
dc.date.issued2023
dc.description.abstractWith the expansion of social networking services, a large number of face images have been disclosed on the Internet.Since face recognition makes it easy to collect face images of specific persons, the collected face images can be used to attack face recognition systems, such as spoofing attacks.Face image de-identification, which makes face recognition difficult without changing the appearance of the face image, is necessary for disclosing face images safely on the Internet.In this paper, we propose a face image de-identification method by embedding facial features of another person into a face image.The proposed method uses a convolutional neural network to generate a face image that can be recognized as that of another person while preserving the appearance of the face image.Through a set of experiments using a public face image dataset, we demonstrate that the proposed method preserves the appearance of face images and has high de-identification performance against unknown face recognition models compared to conventional methods.en
dc.identifier.isbn978-3-88579-733-3
dc.identifier.issn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43259
dc.language.isoen
dc.pubPlaceBonn
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2023
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-339
dc.subjectDe-identification
dc.subjectFace and gesture recognition
dc.titleFace Image De-identification Based on Feature Embedding for Privacy Protectionen
dc.typeText/Conference Paper
mci.conference.date20.-22. September 2023
mci.conference.locationDarmstadt
mci.conference.sessiontitleRegular Research Papers
mci.reference.pages113-122

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild
Name:
LNI_033.pdf
Größe:
519.88 KB
Format:
Adobe Portable Document Format
Herunterladen

Sammlungen

P339 - BIOSIG 2023 - Proceedings of the 22nd International Conference of the Biometrics Special Interest Group