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研究生:黃信笙
研究生(外文):Shin-Sheng Huang
論文名稱:使用正規化鑑別分析與AdaBoost演算法的臉部表情辨識之研究
論文名稱(外文):Facial Expression Recognition by a novel Regularized discriminant analysis with AdaBoost
指導教授:李建誠李建誠引用關係
指導教授(外文):Chien-Cheng Lee
學位類別:碩士
校院名稱:元智大學
系所名稱:通訊工程學系
學門:工程學門
學類:電資工程學類
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:49
中文關鍵詞:臉部表情鑑別分析
外文關鍵詞:facial expressiondiscriminant analysisAdaBoost
相關次數:
  • 被引用被引用:1
  • 點閱點閱:230
  • 評分評分:
  • 下載下載:5
  • 收藏至我的研究室書目清單書目收藏:1
本論文中,我們提出一種全新的方法,來辨識人臉中的七種表情,其中包括生氣、噁心、害怕、開心、難過、驚訝及無情緒的表情。在特徵擷取方面,我們使用局部的Gabor filter來擷取分類的特徵以避免多餘的資訊。接著利用本論文提出的正規化鑑別分析 AdaBoost 演算法 (Regularized Discriminant Analysis-based AdaBoost, RDA-AB)來分類表情。 在RDA-AB中,正規化鑑別分析為在 boosting 程序中的分類器,藉由參數的調整成功地解決了高維度、低資料量及不適定解的問題。我們並利用粒子群最佳化演算法 (particle swarm optimization, PSO) 來尋找最佳的參數。最後,研究結果顯示本論文提出的RDA-AB在臉部表情辨識上有顯著的表現。
This paper presents a novel method for facial expression recognition including happy, disgust, fear, anger, sad, surprise and neutral. The proposed method utilizes a regularized discriminant analysis-based AdaBoost algorithm (RDA-AB) with local Gabor features to recognize the facial expressions. The RDA-AB uses RDA as a learner in the boosting algorithm. The RDA combines the strength of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). It solves the small sample size and ill-posed problems suffered from QDA and LDA using a regularization technique. The proposed method also adopts the particle swarm optimization (PSO) algorithm to estimate optimal parameters in RDA. Experimental results show that the performance of the proposed method is excellent when it is compared with that of other facial expression recognition methods.
書頁名 i
審定書 ii
中文提要 iii
英文提要 iv
誌 謝 v
目 錄 vi
表 目 錄 vii
圖 目 錄 viii
第一章、序論 1
1.1 前言 1
1.2 文獻探討 2
1.3 研究動機與目標 5
1.4 方法及論文架構 6
第二章、臉部表情特徵擷取及其相關研究 9
2.1 臉部表情特徵 9
2.1.1 對臉部特徵的前處理 9
2.2 Gabor 特徵擷取 14
2.2.1 Gabor濾波器 14
第三章、基於正規化鑑別分析結合AdaBoost演算法之討論 18
3.1 鑑別分析方法 18
3.1.1線性鑑別分析及二次鑑別分析 19
3.1.2正規化線性鑑別分析 20
3.1.3 正規化鑑別分析之參數選擇 (Model Selection) 22
3.2 Regularized Discriminant Analysis-based AdaBoost 演算法 24
3.2.1 Boosting程序中的特徵擷取 25
3.2.2 RDA-AB演算法之程序 26
第四章、實驗結果與討論 28
4.1局部Gabor濾波器的選擇 28
4.2 RDA-AB中訓練個數的選擇 34
4.3 與其他相關研究之比較 40
4.4 討論 41
第五章、未來展望與結論 44
參考文獻 45
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