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Color Invariant Skin Segmentation

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dc.contributor.authorXu, Hanen
dc.contributor.authorSarkar, Abhijiten
dc.contributor.authorAbbott, A. Lynnen
dc.date.accessioned2023-02-27T13:32:42Zen
dc.date.available2023-02-27T13:32:42Zen
dc.date.issued2022-06en
dc.date.updated2023-02-25T18:57:34Zen
dc.description.abstractThis paper addresses the problem of automatically detecting human skin in images without reliance on color information. A primary motivation of the work has been to achieve results that are consistent across the full range of skin tones, even while using a training dataset that is significantly biased toward lighter skin tones. Previous skin-detection methods have used color cues almost exclusively, and we present a new approach that performs well in the absence of such information. A key aspect of the work is dataset repair through augmentation that is applied strategically during training, with the goal of color invariant feature learning to enhance generalization. We have demonstrated the concept using two architectures, and experimental results show improvements in both precision and recall for most Fitzpatrick skin tones in the benchmark ECU dataset. We further tested the system with the RFW dataset to show that the proposed method performs much more consistently across different ethnicities, thereby reducing the chance of bias based on skin color. To demonstrate the effectiveness of our work, extensive experiments were performed on grayscale images as well as images obtained under unconstrained illumination and with artificial filters. Source code: https://github.com/HanXuMartin/Color-Invariant-Skin-Segmentationen
dc.description.versionAccepted versionen
dc.format.extentPages 2905-2914en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/CVPRW56347.2022.00328en
dc.identifier.eissn2160-7516en
dc.identifier.isbn9781665487399en
dc.identifier.issn2160-7508en
dc.identifier.orcidAbbott, Amos [0000-0003-3850-6771]en
dc.identifier.urihttp://hdl.handle.net/10919/113968en
dc.identifier.volume2022-Juneen
dc.language.isoenen
dc.publisherIEEEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleColor Invariant Skin Segmentationen
dc.title.serialIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshopsen
dc.typeConference proceedingen
dc.type.dcmitypeTexten
dc.type.otherConference Proceedingen
pubs.finish-date2022-06-20en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Electrical and Computer Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.start-date2022-06-19en

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