Face/object recognition and tracking on IoT device with 3D camera

• 主要作者: Lin, Yuxin
• 其他作者: Chang Chip Hong
• 格式: Final Year Project
• 語言: English
• 出版: Nanyang Technological University 2022
• 主題: Engineering::Electrical and electronic engineering::Computer hardware, software and systems
• 在線閱讀: https://hdl.handle.net/10356/158126
• 機構: Nanyang Technological University

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.