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研究生:任鵬軒
研究生(外文):Peng-Hsuan Jen
論文名稱:利用基因演算法分割與主值成份分析於人臉辨識
論文名稱(外文):Faces Recognition Based on Genetic Algorithms Segmentation and Principal Component Analysis
指導教授:張英德張英德引用關係
指導教授(外文):Ying-De Zhang
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:電機工程學系
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:2008
畢業學年度:96
語文別:中文
論文頁數:84
中文關鍵詞:人臉偵測人臉辨識主值成份分析基因演算法
外文關鍵詞:Face detectionFace recognitionPrincipal Component AnalysisGenetic Algorithms
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本論文利用基因演算法與主值成份分析( Principal Component Analysis , PCA)技術實現一套人臉辨識的自動化系統。此系統主要分為兩部份:人臉偵測與人臉辨識。在人臉偵測部份,本論文採用基因演算法(genetic algorithms) ,利用人臉輪廓及橢圓形狀來正確的偵測出人臉的位置,並準確的圈出人臉。在人臉辨識的部份,先將不同的長寬比例的人臉影像訓練組,利用次取樣與雙線性內插法技術轉換成70*70像素(pixel)大小的影像,然後以PCA方法建構出人臉資料庫,在以歐氏距離(Euclidean distance)的大小來判定人臉身份。
This paper utilize Genetic Algorithms and Principal Component Analyzation to realize an automatic system for face recognition. This system is divided into two sections: the faces detection and the face recognition. On the part of faces detection, we detect the facial areas with the genetic algorithms to find the best-fit ellipse in order to cover the face contour that can include the greater area of face outline.On the part of face recognition, the facial image training sets are set to the size of 70x70 pixels by subsampling and Bilinear Interpolation. Finally, we use PCA method to compare the facial images with data bank, minimum Euclidean distance is used as the criterion for facial recognition.
中文摘要 …………………………………………………………………………I

英文摘要 …………………………………………………………………………II

誌謝 .……………………………………………………………………………III

目錄 ………………………………………………………………………………VI

圖片索引 …………………………………………………………………………V

第一章 緒論…………………………………………………………………1
1.1 研究動機與目的 …………………………………………………1
1.2 相關研究回顧 ……………………………………………………3
1.2.1 人臉偵測相關研究 ………………………………………4
1.2.2 人臉辨識相關研究 ………………………………………5
1.3 論文架構 …………………………………………………………7
第二章 基因演算法之人臉偵測 ……………………………………………9
2.1 顏色分割 …………………………………………………………9
2.2 人臉區域判定 ……………………………………………………12
2.2.1 統一影像規格 ……………………………………………13
     2.2.2 YCBCR 膚色偵測與Canny邊緣檢測器…………………14
   2.2.3 雜訊移除…………………………… ……………………24
   2.2.4 人臉次取樣與拉高及銳化 ………………………………26
  2.2.5 基因演算法 ………………………………………………29
  2.2.6 人臉擷取及解壓縮 ………………………………………40
第三章 人臉辨識 ………………………………………………………………43
3.1主值成分分析原理 …………………………………………………43
3.2 PCA 用於人臉辨識的原理 ………………………………………47
3.3以歐氏距離作為人臉辨識決策方法 ………………………………51
第四章 多人臉部辨識 ………………………………………………………52
4.1 多人臉部偵測流程與步驟 ………………………………………52
4.2 人臉資料庫的建構 ………………………………………………54
4.3 多人臉部辨識完整流程 …………………………………………60

第五章 實驗結果 ………………………………………………………………64
5.1 人臉偵測測驗結果與討論 ……………………………………64
5.1.1 多人臉部偵測實驗結果 …………………………………64
5.1.2人臉偵測實驗結果討論 …………………………………75
5.2 人臉辨識結果與討論 ………………………………………77
5.2.1 人臉辨識結果 ……………………………………………77
5.2.2 人臉辨識結果討論 ………………………………………79
第六章 結論與未來研究方向 …………………………………………………80
6.1結論 ………………………………………………………………80
6.2 未來展望……………………………………………………………80
參考文獻 ………………………………………………………………………81
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