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研究生:方國意
研究生(外文):Guo-Yi Fanh
論文名稱:基於特徵自動定位之人臉表情辨識系統之實現
論文名稱(外文):An Implementation of Facial Expression Recognition System Base on Automatic Locating Features
指導教授:王明習
指導教授(外文):Ming-Shi Wang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:工程科學系碩博士班
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:83
中文關鍵詞:自商影像表情辨識系統矩形特徵賈伯濾波器
外文關鍵詞:Self-quotient imageFacial expression recognitionRectangle featureGabor filter
相關次數:
  • 被引用被引用:11
  • 點閱點閱:535
  • 評分評分:
  • 下載下載:195
  • 收藏至我的研究室書目清單書目收藏:1
在本論文中,提出了自動化人臉表情辨識系統,本系統無需使用者額外的輔助就可以針對輸入的影像進行表情特徵的擷取及辨識;系統之主要概念是以表情影響比較大的臉部肌肉紋理為依據。系統分為三個部分,分別為「臉部偵測」、「特徵值擷取與計算」及「表情辨識」。在臉部偵測上利用矩形特徵結合自適性布斯特(Adaboost)演算法來偵測及定位出臉部的位置,並切割出正面臉部影像;在特徵值擷取與計算上,首先是先將臉部影像由上而下依序取得眉毛、眼睛、鼻頭及嘴巴等之區域,並分別切割出來,再根據各特徵部位之影像內的紋理來定位特徵點,這些特徵點,主要位於各特徵部位肌肉變化較劇烈的地方;有了特徵點後,利用特徵點的資訊,分別計算由無表情到表情變化完成,兩者間各個特徵點位置的變化,再加上各特徵點上的區域紋理,組合成人臉表情的特徵向量,之後利用SVM進行分類;由實驗結果顯示,本系統在利用JAFFE人臉表情資料庫測試可以達到93%之辨識率。
In this, we propose an automatic facial expression recognition system. The system can auto-extract the features and recognition the expression. The main concept of the system is based on the facial muscle texture of facial expression. The system is divided into three parts. First part is Face Detection. Second part is Feature Extraction and Calculation. Third part is Facial Expression Recognition. In the face detection, it use the rectangle features and Adaboost algorithm to detect the facial region and segment the front facial image. In the feature extraction and calculation, first we extract these facial organ image in order from top to button. These facial organ image are eyebrows, eyes, nose and mouth region. Second we use facial organ image to locate the feature points, respectively. These feature points are on the more changed muscle texture in the face. After we get these feature points, we use the feature point location change from neutral expression to special expression(example Surprise) and the texture around the feature point location as the feature vector of facial expression. Then we use the support vector machine to classify the expression. In the experiment result, we use JAFFE database as training and testing data, and we get recognition rate of 93%.
摘要 i
Abstract ii
誌謝 iv
目錄 iv
圖目錄 v
表目錄 vii
第一章 緒論 1
1.1 研究動機與目的 1
1.2 相關研究 2
1.3 論文大綱 10
第二章 背景知識 11
2.1. 人臉偵測 11
2.1.1 矩形特徵 12
2.1.2 積分影像 17
2.1.3 自適性布斯特演算法 25
2.1.4 瀑布模型及可變子視窗 27
2.2. 賈伯濾波 30
2.3. 自商影像 32
2.4. 支援向量機 36
2.4.1 線性可分支援向量機 38
2.4.2 線性不可分支援向量機 40
2.4.3 非線性支援向量機 42
第三章 人臉表情辨識 45
3.1. 前處理及臉部偵測 47
3.2. 特徵點定位 53
3.3. 特徵向量計算 68
第四章 實驗結果與討論 74
4.1 系統環境 74
4.2 實驗結果 76
第五章 結論於未來研究方向 80
5.1 結論 80
5.2 未來展望 80
參考文獻 82
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