Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156565
Title: Towards interpretable & robust face recognition
Authors: Pattra, Surya Paryanta
Keywords: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Pattra, S. P. (2022). Towards interpretable & robust face recognition. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156565
Project: SCSE21-0110
Abstract: With 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.
URI: https://hdl.handle.net/10356/156565
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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