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dc.contributor.authorPattra, Surya Paryantaen_US
dc.identifier.citationPattra, S. P. (2022). Towards interpretable & robust face recognition. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractWith the recent advancements of deep learning in computer vision, current state-of-the-art face recognition algorithms have surpassed human-level performance. However, they are not robust against constrained environments, especially image occlusions. To tackle the existing problem of occluded face recognition with facial masks, existing approaches utilize masks-detector module to detect and filter out the masks. In addition, those methods are trained using occluded version of datasets. Our proposed architecture, however, is able to be trained on general face dataset and generalize well into facial-masks occlusion. We also showed that our solution could surpass previous baselines.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Image processing and computer visionen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleTowards interpretable & robust face recognitionen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLin Weisien_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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