Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158126
Title: Face/object recognition and tracking on IoT device with 3D camera
Authors: Lin, Yuxin
Keywords: Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Lin, 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/10356/158126
Abstract: Traditional 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.
URI: https://hdl.handle.net/10356/158126
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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