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On Recognizing Occluded Faces in the Wild

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dc.contributor.authorErakın, Mustafa Ekrem
dc.contributor.authorDemir, Uğur
dc.contributor.authorEkenel, Hazım Kemal
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDamer, Naser
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorGomez-Barrero, Marta
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorSequeira, Ana
dc.contributor.editorUhl, Andreas
dc.date.accessioned2021-10-04T08:43:45Z
dc.date.available2021-10-04T08:43:45Z
dc.date.issued2021
dc.description.abstractFacial appearance variations due to occlusion has been one of the main challenges for face recognition systems. To facilitate further research in this area, it is necessary and important to have occluded face datasets collected from real-world, as synthetically generated occluded faces cannot represent the nature of the problem. In this paper, we present the Real World Occluded Faces (ROF) dataset, that contains faces with both upper face occlusion, due to sunglasses, and lower face occlusion, due to masks. We propose two evaluation protocols for this dataset. Benchmark experiments on the dataset have shown that no matter how powerful the deep face representation models are, their performance degrades significantly when they are tested on real-world occluded faces. It is observed that the performance drop is far less when the models are tested on synthetically generated occluded faces. The ROF dataset and the associated evaluation protocols are publicly available at the following link https://github.com/ekremerakin/RealWorldOccludedFaces.en
dc.identifier.isbn978-3-88579-709-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37453
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-317
dc.subjectFace recognition
dc.subjectface occlusion
dc.subjectdeep learning
dc.subjectreal-world occluded faces
dc.titleOn Recognizing Occluded Faces in the Wilden
dc.typeText/Conference Paper
gi.citation.endPage196
gi.citation.publisherPlaceBonn
gi.citation.startPage189
gi.conference.date15.-17. September 2021
gi.conference.locationInternational Digital Conference
gi.conference.sessiontitleFurther Conference Contributions

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P315 - BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group