Human Face Identical using Support Vector Machine
碩士 === 國立勤益科技大學 === 電子工程系 === 105 === In this paper, the human face recognition is used for the purpose of research. The study takes two corners and the center point of eyes in the face, and two corners and the center point of the mouth as the feature points. The six feature points connected to each...
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ndltd-TW-105NCIT57750292019-05-16T00:15:12Z http://ndltd.ncl.edu.tw/handle/5t5894 Human Face Identical using Support Vector Machine 使用支持向量機技術之人臉影像辨識 Jyun-Siang Yang 楊竣翔 碩士 國立勤益科技大學 電子工程系 105 In this paper, the human face recognition is used for the purpose of research. The study takes two corners and the center point of eyes in the face, and two corners and the center point of the mouth as the feature points. The six feature points connected to each other are used as the eigenvector, and, finally, the face image recognition is completed by support vector machine technology combined with the eigenvector. In the course of the experiment, the face’s upper and lower parts respectively adapt skin color classification and texture technology, and use the histogram technique to find the ROI area of the eye and mouth. In the ROI area, Sobel Edge Detection is used to find two corners of eyes and two corners of the mouth (a total of four feature points),, and then the center point of the two corners separately (a total of 2 feature points). These six feature points are used to calculate the six eigenvectors as the input parameters of the support vector machine. Finally, the face vector recognition is successfully performed by the support vector machine. It is proved that the method can correctly identify the target face in different face data. Wen-Yuan Chen 陳文淵 2017 學位論文 ; thesis 71 zh-TW |
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碩士 === 國立勤益科技大學 === 電子工程系 === 105 === In this paper, the human face recognition is used for the purpose of research. The study takes two corners and the center point of eyes in the face, and two corners and the center point of the mouth as the feature points. The six feature points connected to each other are used as the eigenvector, and, finally, the face image recognition is completed by support vector machine technology combined with the eigenvector. In the course of the experiment, the face’s upper and lower parts respectively adapt skin color classification and texture technology, and use the histogram technique to find the ROI area of the eye and mouth. In the ROI area, Sobel Edge Detection is used to find two corners of eyes and two corners of the mouth (a total of four feature points),, and then the center point of the two corners separately (a total of 2 feature points). These six feature points are used to calculate the six eigenvectors as the input parameters of the support vector machine. Finally, the face vector recognition is successfully performed by the support vector machine. It is proved that the method can correctly identify the target face in different face data.
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author2 |
Wen-Yuan Chen |
author_facet |
Wen-Yuan Chen Jyun-Siang Yang 楊竣翔 |
author |
Jyun-Siang Yang 楊竣翔 |
spellingShingle |
Jyun-Siang Yang 楊竣翔 Human Face Identical using Support Vector Machine |
author_sort |
Jyun-Siang Yang |
title |
Human Face Identical using Support Vector Machine |
title_short |
Human Face Identical using Support Vector Machine |
title_full |
Human Face Identical using Support Vector Machine |
title_fullStr |
Human Face Identical using Support Vector Machine |
title_full_unstemmed |
Human Face Identical using Support Vector Machine |
title_sort |
human face identical using support vector machine |
publishDate |
2017 |
url |
http://ndltd.ncl.edu.tw/handle/5t5894 |
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