A design of face recognition system
碩士 === 國立中山大學 === 電機工程學系研究所 === 91 === The design of a face recognition system ( FRS ) can been separated into two major modules – face detection and face recognition. In the face detection part, we combine image pre-processing techniques with maximum-likelihood estimation to detect the nearest fro...
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ndltd-TW-091NSYS54421062016-06-22T04:20:47Z http://ndltd.ncl.edu.tw/handle/84219264468812793435 A design of face recognition system 人臉辨識系統之設計研究 Ming-Hong Jiang 姜明宏 碩士 國立中山大學 電機工程學系研究所 91 The design of a face recognition system ( FRS ) can been separated into two major modules – face detection and face recognition. In the face detection part, we combine image pre-processing techniques with maximum-likelihood estimation to detect the nearest frontal face in a single image. Under limited restrictions, our detection method overcomes some of the challenging tasks, such as variability in scale, location, orientation, facial expression, occlusion ( glasses ), and lighting change. In the face recognition part, we use both Karhunen-Loeve transform and linear discrimant analysis ( LDA ) to perform feature extraction. In this feature extraction process, the features are calculated from the inner products of the original samples and the selected eigenvectors. In general, as the size of the face database is increased, the recognition time will be proportionally increased. To solve this problem, hard-limited Karhunen-Loeve transform ( HLKLT ) is applied to reduce the computation time in our FRS. Chih-Chien Chen 陳志堅 2003 學位論文 ; thesis 53 zh-TW |
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碩士 === 國立中山大學 === 電機工程學系研究所 === 91 === The design of a face recognition system ( FRS ) can been separated into two major modules – face detection and face recognition.
In the face detection part, we combine image pre-processing techniques with maximum-likelihood estimation to detect the nearest frontal face in a single image. Under limited restrictions, our detection method overcomes some of the challenging tasks, such as variability in scale, location, orientation, facial expression, occlusion ( glasses ), and lighting change.
In the face recognition part, we use both Karhunen-Loeve transform and linear discrimant analysis ( LDA ) to perform feature extraction. In this feature extraction process, the features are calculated from the inner products of the original samples and the selected eigenvectors. In general, as the size of the face database is increased, the recognition time will be proportionally increased. To solve this problem, hard-limited Karhunen-Loeve transform ( HLKLT ) is applied to reduce the computation time in our FRS.
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Chih-Chien Chen |
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Chih-Chien Chen Ming-Hong Jiang 姜明宏 |
author |
Ming-Hong Jiang 姜明宏 |
spellingShingle |
Ming-Hong Jiang 姜明宏 A design of face recognition system |
author_sort |
Ming-Hong Jiang |
title |
A design of face recognition system |
title_short |
A design of face recognition system |
title_full |
A design of face recognition system |
title_fullStr |
A design of face recognition system |
title_full_unstemmed |
A design of face recognition system |
title_sort |
design of face recognition system |
publishDate |
2003 |
url |
http://ndltd.ncl.edu.tw/handle/84219264468812793435 |
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