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dc.contributor.authorWu, Sibingen_US
dc.identifier.citationWu, S. (2022). Digital makeup using machine learning algorithms. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractIn this report, we present a pipeline system of digital makeup for industry scenarios. The pipeline contains two parts: i) facial feature semantic segmentation; ii) colour transfer. For facial feature semantic segmentation task, we adopt fully convolutional network (FCN) with weighted cross entropy as loss function during training; for colour transfer task, we experimented N-dimensional Probability Density Function transfer algorithm, a fast exemplar-based image colourisation approach using colour embeddings named Color2Embed, and deep exemplar-bases colourisation approach. Considering economical and qualitative factors, we conclude that model trained by VGG16 FCN with weighted cross entropy together with fast exemplar-based image colourisation yields the most suitable result.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleDigital makeup using machine learning algorithmsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorHe Yingen_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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