Face/object recognition and tracking on IoT device with 3D camera
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 learni...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/158126 |
| _version_ | 1824455364677992448 |
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| author | Lin, Yuxin |
| author2 | Chang Chip Hong |
| author_facet | Chang Chip Hong Lin, Yuxin |
| author_sort | Lin, Yuxin |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T03:37:02Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/158126 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:37:02Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1581262023-07-07T19:24:31Z Face/object recognition and tracking on IoT device with 3D camera Lin, Yuxin Chang Chip Hong School of Electrical and Electronic Engineering Centre for Integrated Circuits and Systems ECHChang@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems 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. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-30T11:43:10Z 2022-05-30T11:43:10Z 2022 Final Year Project (FYP) 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 https://hdl.handle.net/10356/158126 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Computer hardware, software and systems Lin, Yuxin Face/object recognition and tracking on IoT device with 3D camera |
| title | Face/object recognition and tracking on IoT device with 3D camera |
| title_full | Face/object recognition and tracking on IoT device with 3D camera |
| title_fullStr | Face/object recognition and tracking on IoT device with 3D camera |
| title_full_unstemmed | Face/object recognition and tracking on IoT device with 3D camera |
| title_short | Face/object recognition and tracking on IoT device with 3D camera |
| title_sort | face object recognition and tracking on iot device with 3d camera |
| topic | Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | https://hdl.handle.net/10356/158126 |
| work_keys_str_mv | AT linyuxin faceobjectrecognitionandtrackingoniotdevicewith3dcamera |