Logo des Repositoriums
 

Can Generative Colourisation Help Face Recognition?

Kurzanzeige
dc.contributor.authorDrozdowski, Pawel
dc.contributor.authorFischer, Daniel
dc.contributor.authorRathgeb, Christian
dc.contributor.authorGeissler, Julian
dc.contributor.authorKnedlik, Jan
dc.contributor.authorBusch, Christoph
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2020-09-16T08:25:48Z
dc.date.available2020-09-16T08:25:48Z
dc.date.issued2020
dc.description.abstractGenerative colourisation methods can be applied to automatically convert greyscale images to realistically looking colour images. In a face recognition system, such techniques might be employed as a pre-processing step in scenarios where either one or both face images to be compared are only available in greyscale format. In an experimental setup which reflects said scenarios, we investigate if generative colourisation can improve face sample utility and overall biometric performance of face recognition. To this end, subsets of the FERET and FRGCv2 face image databases are converted to greyscale and colourised applying two versions of the DeOldify colourisation algorithm. Face sample quality assessment is done using the FaceQnet quality estimator. Biometric performance measurements are conducted for the widely used ArcFace system with its built-in face detector and reported according to standardised metrics. Obtained results indicate that, for the tested systems, the application of generative colourisation does neither improve face image quality nor recognition performance. However, generative colourisation was found to aid face detection and subsequent feature extraction of the used face recognition system which results in a decrease of the overall false reject rate.en
dc.identifier.isbn978-3-88579-700-5
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34341
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-306
dc.subjectbiometrics
dc.subjectface recognition
dc.subjectface image quality
dc.subjectgenerative colourisation
dc.titleCan Generative Colourisation Help Face Recognition?en
dc.typeText/Conference Paper
gi.citation.endPage307
gi.citation.publisherPlaceBonn
gi.citation.startPage299
gi.conference.date16.-18. September 2020
gi.conference.locationInternational Digital Conference
gi.conference.sessiontitleFurther Conference Contributions

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
BIOSIG_2020_paper_1_update.pdf
Größe:
1.53 MB
Format:
Adobe Portable Document Format
Herunterladen

Sammlungen

P306 - BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group