The Study of Automatic Feature Capture System for 3D Face Recognition
碩士 === 國立臺北科技大學 === 自動化科技研究所 === 94 === This research develops a system which can recognize three-dimensional human facial expression. In this study, Dynamic Skin Color Scale (DSCS) and Grey Edge Method are used to capture the key feature points. DSCS techniques have the advantage of less effect by...
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ndltd-TW-094TIT051460142019-06-27T05:09:01Z http://ndltd.ncl.edu.tw/handle/6eu8ju The Study of Automatic Feature Capture System for 3D Face Recognition 三維人臉辨識自動特徵擷取系統之研究 Yan Jie Huang 黃彥傑 碩士 國立臺北科技大學 自動化科技研究所 94 This research develops a system which can recognize three-dimensional human facial expression. In this study, Dynamic Skin Color Scale (DSCS) and Grey Edge Method are used to capture the key feature points. DSCS techniques have the advantage of less effect by light source. In order to effectively obtain the feature point and minimize the error comparison, the recognition process is divided into two steps and combined with RGB contrast enhancement. In addition, some affine coefficient invariable theorem are used as the basis for recognition. The selection of system reference plan is at the eye position, therefore, the feature point will not missing when the face is moving. In order to enhance the robust recognition, many types of expression training are added to each Relative Affine coefficient. 吳明川 2006 學位論文 ; thesis 84 zh-TW |
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碩士 === 國立臺北科技大學 === 自動化科技研究所 === 94 === This research develops a system which can recognize three-dimensional human facial expression. In this study, Dynamic Skin Color Scale (DSCS) and Grey Edge Method are used to capture the key feature points. DSCS techniques have the advantage of less effect by light source. In order to effectively obtain the feature point and minimize the error comparison, the recognition process is divided into two steps and combined with RGB contrast enhancement. In addition, some affine coefficient invariable theorem are used as the basis for recognition. The selection of system reference plan is at the eye position, therefore, the feature point will not missing when the face is moving. In order to enhance the robust recognition, many types of expression training are added to each Relative Affine coefficient.
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吳明川 |
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吳明川 Yan Jie Huang 黃彥傑 |
author |
Yan Jie Huang 黃彥傑 |
spellingShingle |
Yan Jie Huang 黃彥傑 The Study of Automatic Feature Capture System for 3D Face Recognition |
author_sort |
Yan Jie Huang |
title |
The Study of Automatic Feature Capture System for 3D Face Recognition |
title_short |
The Study of Automatic Feature Capture System for 3D Face Recognition |
title_full |
The Study of Automatic Feature Capture System for 3D Face Recognition |
title_fullStr |
The Study of Automatic Feature Capture System for 3D Face Recognition |
title_full_unstemmed |
The Study of Automatic Feature Capture System for 3D Face Recognition |
title_sort |
study of automatic feature capture system for 3d face recognition |
publishDate |
2006 |
url |
http://ndltd.ncl.edu.tw/handle/6eu8ju |
work_keys_str_mv |
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