Face Recognition Using Tied Factor Analysis
碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 101 === Recently, there is a significant progress in study of face recognition. Meanwhile, hardware technology advances, too. The face recognition has been applied on more areas. In order to make the face recognition practical, it is required to reduce the effect of...
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ndltd-TW-101NCKU53920732015-10-13T22:51:44Z http://ndltd.ncl.edu.tw/handle/42268947524139763248 Face Recognition Using Tied Factor Analysis 使用定因素分析之人臉辨識 Chia-LungHsu 徐嘉隆 碩士 國立成功大學 資訊工程學系碩博士班 101 Recently, there is a significant progress in study of face recognition. Meanwhile, hardware technology advances, too. The face recognition has been applied on more areas. In order to make the face recognition practical, it is required to reduce the effect of variations on performance. In this thesis, we focus on the pose variations problem and take Tied Factor Analysis as the core of system. The concept is to explain the complex distribution caused by pose variations with several Factor Analysis models. Moreover, there exists a certain representation for images that at different poses from the same subject without regard to pose and facial variations. In learning process, the EM algorithm is used to find the optimal parameters iteratively. In recognition process, a variety of generative models are designed to explain the different generative procedures of data, and then take the most likely procedure as the result. To increase the tolerance for variations, we use the Affine Transform normalization to reduce the effect of pose variations. And we add various variations to promote the performance. Jenn-Jier Lien 連振杰 2013 學位論文 ; thesis 56 en_US |
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碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 101 === Recently, there is a significant progress in study of face recognition. Meanwhile, hardware technology advances, too. The face recognition has been applied on more areas. In order to make the face recognition practical, it is required to reduce the effect of variations on performance. In this thesis, we focus on the pose variations problem and take Tied Factor Analysis as the core of system. The concept is to explain the complex distribution caused by pose variations with several Factor Analysis models. Moreover, there exists a certain representation for images that at different poses from the same subject without regard to pose and facial variations. In learning process, the EM algorithm is used to find the optimal parameters iteratively. In recognition process, a variety of generative models are designed to explain the different generative procedures of data, and then take the most likely procedure as the result. To increase the tolerance for variations, we use the Affine Transform normalization to reduce the effect of pose variations. And we add various variations to promote the performance.
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Jenn-Jier Lien |
author_facet |
Jenn-Jier Lien Chia-LungHsu 徐嘉隆 |
author |
Chia-LungHsu 徐嘉隆 |
spellingShingle |
Chia-LungHsu 徐嘉隆 Face Recognition Using Tied Factor Analysis |
author_sort |
Chia-LungHsu |
title |
Face Recognition Using Tied Factor Analysis |
title_short |
Face Recognition Using Tied Factor Analysis |
title_full |
Face Recognition Using Tied Factor Analysis |
title_fullStr |
Face Recognition Using Tied Factor Analysis |
title_full_unstemmed |
Face Recognition Using Tied Factor Analysis |
title_sort |
face recognition using tied factor analysis |
publishDate |
2013 |
url |
http://ndltd.ncl.edu.tw/handle/42268947524139763248 |
work_keys_str_mv |
AT chialunghsu facerecognitionusingtiedfactoranalysis AT xújiālóng facerecognitionusingtiedfactoranalysis AT chialunghsu shǐyòngdìngyīnsùfēnxīzhīrénliǎnbiànshí AT xújiālóng shǐyòngdìngyīnsùfēnxīzhīrénliǎnbiànshí |
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1718081371929313280 |