What the eye doesn’t see : using infrared to improve face recognition of individuals with highly pigmented skin

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dc.contributor.advisorTheart, Rensuen_ZA
dc.contributor.advisorBooysen, Thinusen_ZA
dc.contributor.authorMuthua, Alexen_ZA
dc.contributor.otherStellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.en_ZA
dc.date.accessioned2022-11-16T06:58:17Zen_ZA
dc.date.accessioned2023-01-16T12:45:16Zen_ZA
dc.date.available2022-11-16T06:58:17Zen_ZA
dc.date.available2023-01-16T12:45:16Zen_ZA
dc.date.issued2022-12en_ZA
dc.descriptionThesis (MEng) -- Stellenbosch University, 2022.en_ZA
dc.description.abstractENGLISH ABSTRACT: Face recognition technology has become commonplace in security and access control applications. However, their performance leaves a lot to be desired when working with highly pigmented skin tones. One reason for this is the training bias introduced by under-representation in existing datasets. The other is inherent to pigmentation – darker skins absorb more light and therefore could reflect l ess d iscernible d etail i n t he v isible s pectrum. We s how how this can be enhanced by incorporating the infrared spectrum, which electronic sensors can perceive. We collect a database with images of highly pigmented individuals, captured using the visible, infrared and full spectra We fine-tune state-of-the-art face recognition systems and compare the performance of these three spectra. We also assess the impact of narrow and wide cropping, different facial orientations, and sunlit and shaded conditions. We find a marked improvement in the accuracy and in the AUC values of the ROC curves when including the infrared spectrum, with performance increasing from 97.5% to 99.1% for highly pigmented faces. Including different facial orientations and narrow cropping also improves the performance, and can therefore be deemed as recommended best practices. Analysis of the activation maps of the CNNs finds t hat fi ne-tuning mo dels ac tivate mo re ge nerally ov er al l re gions of the face while models with pre-trained weights, focus on fewer features with higher activation intensity values over those regions. In both cases, the nose region appears as the most important feature for face recognition for highly pigmented faces.en_ZA
dc.description.versionMastersen_ZA
dc.format.extentxi, 113 pages : illustrationsen_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/126005en_ZA
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subjectInfrared spectroscopyen_ZA
dc.subjectHuman face recognition (Computer science)en_ZA
dc.subjectComputers -- Access controlen_ZA
dc.subjectHuman skin coloren_ZA
dc.subjectUCTDen_ZA
dc.titleWhat the eye doesn’t see : using infrared to improve face recognition of individuals with highly pigmented skinen_ZA
dc.typeThesisen_ZA
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Masters Degrees (Electrical and Electronic Engineering)