Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158126
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dc.contributor.authorLin, Yuxinen_US
dc.date.accessioned2022-05-30T11:43:10Z-
dc.date.available2022-05-30T11:43:10Z-
dc.date.issued2022-
dc.identifier.citationLin, Y. (2022). Face/object recognition and tracking on IoT device with 3D camera. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158126en_US
dc.identifier.urihttps://hdl.handle.net/10356/158126-
dc.description.abstractTraditional face recognition systems often use RGB images as input for feature extraction and classification. However, with the gradually decreasing cost of depth sensors, RGB-Depth(D) images captured using low-cost sensors are becoming comparably easy to acquire. This project proposes a deep learning face recognition model for RGB-D images and deploys the developed model onto the proposed CUDA accelerated IoT platform. The proposed Local Binary Pattern (LBP)-Depth-guided attention model extracts features on RGB, depth and LBP images and utilizes feature-level fusion mechanism to guide the attention on RGB images. Compared with Depth-guided Attention, both quantitative and qualitative experiment results indicate that LBP-Depth-guided Attention has better focus on the important discriminative regions and achieved improved recognition accuracies under several challenging conditions, such as occlusion, pose variation and facial expression changes.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineering::Computer hardware, software and systemsen_US
dc.titleFace/object recognition and tracking on IoT device with 3D cameraen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorChang Chip Hongen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.researchCentre for Integrated Circuits and Systemsen_US
dc.contributor.supervisoremailECHChang@ntu.edu.sgen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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