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dc.contributor.author曾茂清en_US
dc.contributor.authorTseng, Moa-Chingen_US
dc.contributor.author林松山en_US
dc.contributor.authorLin, Song-Sunen_US
dc.date.accessioned2014-12-12T01:57:21Z-
dc.date.available2014-12-12T01:57:21Z-
dc.date.issued2011en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT079920507en_US
dc.identifier.urihttp://hdl.handle.net/11536/49696-
dc.description.abstract本論文我們在一開始介紹了許多局部形狀特徵描述子,我們根據許多文獻整理出了常見的一些描述子的概念、特性和缺點。接下來我們針對臉部的正面掃描介紹許多基於不同描述子而衍生出的演算法和方法,之後我們討論了由不同姿態與表情變化所衍生出來的問題。我們對於正面與不同姿態的臉部影像整理出了一些結論並對於受表情影響很大的嘴唇特徵點的提取提出一些想法,希望可以在未來加以實現。zh_TW
dc.description.abstractIn this study, we give an overview of some common local shape feature descriptors. Their concepts, properties and shortcomings are organized according to lots of literature. We then provide a discussion of facial feature extraction methods. Based on different local feature descriptors, we enumerate the corresponding methods and algorithms for the frontal facial scan. Then we discuss the problems caused by changing pose and expression variation respectively in detail and propose some ideals to address the problems. We conclude with a summary and promising future research directions for solving the problem of mouth feature points extraction.en_US
dc.language.isoen_USen_US
dc.subject特徵點zh_TW
dc.subjectFeature pointsen_US
dc.title不受表情影響的人臉配對特徵點抓取方法研究zh_TW
dc.titleA Study on Expression Invariant Feature Points for Human Faces Matchingen_US
dc.typeThesisen_US
dc.contributor.department應用數學系數學建模與科學計算碩士班zh_TW
Appears in Collections:Thesis


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