3D face recognition
This is a face recognition project. It requires programming in C, also adopt PCA for the face recognition algorithm, and knowledge of OpenCV. In this Thesis, we study the face recognition system based on 2D recognition methods and 3D face data. 2 sets of stereo cameras are used to acquire the 2D and...
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sg-ntu-dr.10356-462122023-07-07T16:01:41Z 3D face recognition Li, Shan. Wang Han School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::biometrics This is a face recognition project. It requires programming in C, also adopt PCA for the face recognition algorithm, and knowledge of OpenCV. In this Thesis, we study the face recognition system based on 2D recognition methods and 3D face data. 2 sets of stereo cameras are used to acquire the 2D and 3D face data of a human subject. By applying 2D face detection on the images, the face was first located in the 2D domain. After mapping the 2D location to 3D, 3D faces points cloud are generated from each stereo camera. Then the two 3D data could be merged together. The orientation of the face is approximated by the relative position of the face to the two stereo cameras, and a frontal face is generated based on this approximation. And at last face recognition methods are then applied on the generated 2D image. The efficiency of the whole system algorithm was demonstrated with experimental result in different poses. Bachelor of Engineering 2011-07-07T01:14:11Z 2011-07-07T01:14:11Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/46212 en Nanyang Technological University 47 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::biometrics Li, Shan. 3D face recognition |
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This is a face recognition project. It requires programming in C, also adopt PCA for the face recognition algorithm, and knowledge of OpenCV. In this Thesis, we study the face recognition system based on 2D recognition methods and 3D face data. 2 sets of stereo cameras are used to acquire the 2D and 3D face data of a human subject. By applying 2D face detection on the images, the face was first located in the 2D domain. After mapping the 2D location to 3D, 3D faces points cloud are generated from each stereo camera. Then the two 3D data could be merged together. The orientation of the face is approximated by the relative position of the face to the two stereo cameras, and a frontal face is generated based on this approximation. And at last face recognition methods are then applied on the generated 2D image. The efficiency of the whole system algorithm was demonstrated with experimental result in different poses. |
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Wang Han |
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Wang Han Li, Shan. |
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Final Year Project |
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Li, Shan. |
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Li, Shan. |
title |
3D face recognition |
title_short |
3D face recognition |
title_full |
3D face recognition |
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3D face recognition |
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3D face recognition |
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3d face recognition |
publishDate |
2011 |
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http://hdl.handle.net/10356/46212 |
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1772827603353206784 |