Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163033
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dc.contributor.authorTan, Yi Hengen_US
dc.date.accessioned2022-11-18T04:25:06Z-
dc.date.available2022-11-18T04:25:06Z-
dc.date.issued2022-
dc.identifier.citationTan, Y. H. (2022). Masked face detection with anti-spoofing. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163033en_US
dc.identifier.urihttps://hdl.handle.net/10356/163033-
dc.description.abstractModern facial recognition models have excellent performance identifying cleaned, unobstructed faces. However, limitations arise when these models are faced with novel occlusion conditions. This is a concern as occluded faces are common, especially during the Coronavirus Pandemic where facial masks are required in most settings. Masked faces hinder the performance of facial recognition models in carrying out important tasks. In this project, we will dive into details on modern neural network architecture that deals with occlusion conditions and understand their limitations. The focus is primarily on two recent research, FROM and TDMPNet architecture, that have made significant advancement in detecting occluded faces. The project will leverage on the key techniques learned to better detect occlusion patterns on masked faces. Our results show that the Attention Map produced has good performance in detecting occlusion patterns but further fine tuning is necessary.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationSCSE21 – 0695en_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Image processing and computer visionen_US
dc.titleMasked face detection with anti-spoofingen_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 Businessen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.supervisoremailWSLin@ntu.edu.sgen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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