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研究生: 邱柏智
BO-JR Chiou
論文名稱: 基於主成份分析法與灰關聯分析法之動態人臉辨識
Dynamic Face Recognition based on PCA and GRA
指導教授: 葉榮木
Yeh, Zong-Mu
蔡俊明
Tsai, Chun-Ming
學位類別: 碩士
Master
系所名稱: 機電工程學系
Department of Mechatronic Engineering
論文出版年: 2007
畢業學年度: 95
語文別: 中文
論文頁數: 69
中文關鍵詞: 人臉偵測人臉辨識小波轉換主成份分析法灰關聯分析法特徵臉
英文關鍵詞: Face Detection, Face Recognition, Wavelet Transformation, Principal Component Analysis,, Grey Relational Analysis, Eigenface
論文種類: 學術論文
相關次數: 點閱:154下載:15
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  • 人臉辨識系統廣泛地應用於身分認證、門禁管理與人機界面等領域,近年來由於「智慧生活」科技的提倡,人臉辨識技術已延伸至人與機器最佳化介面之應用。此外視訊會議、影像內容檢索與醫學影像處理等方面,亦是其重要之應用領域。
    本篇論文分為人臉偵測和人臉辨識兩大部分。在人臉偵測的部份,我們利用膚色分割和連通成份的方法找出人臉候選區,再使用色彩分析的方法從人臉候選區中尋找眼睛和嘴唇的特徵,最後再使用眼睛和嘴唇的幾何條件關係去定位出正確的人臉位置。在人臉辨識部分,我們提出一套結合主成份分析法與灰關聯分析法的人臉辨識方法,此方法的架構分為以下三個階段:首先,在影像前處理的階段,我們使用二維小波轉換,對輸入影像做資料壓縮的處理,接著,利用主成份分析法將壓縮過的人臉影像,投影到低維度的子空間中,計算出具有代表性的特徵臉,最後,再使用灰關聯分析法,來辨識出正確的人臉圖片。
    為了驗證本篇所提出的方法,在靜態辨識實驗中,我們使用ORL人臉資料庫,做了一些分析和比較的實驗,實驗結果證明,在40人條件下,訓練樣本為五張時,可以得到91.6%的辨識率。而本篇方法在動態辨識實驗中以不同距離拍攝人臉,在30人條件下,可以得到八成以上的辨識率。

    Face recognition systems have been utilized in areas such as biometric identity authentification, acess surveillance, and human-computer interface. More recently, because of the promotion of “intelligent life”, the use of face recognition techniques has been extended to optimizing the human-computer interface. In addition, video conferencing, image content indexing and medical diagnostics are other applications for face recognition.
    This paper first discusses the face detection part, and then discusses the face recognition part. For the face detection part, we used skin color segmentation and connected component method to extract a face candidate. Then color analysis was used to identify the features (lips, eyes) of the face candidate. Finally, measurements related to the eyes and mouth were used to locate the position of the face.
    For the face recognition part, we present a hybrid face recognition method, which combines Principal Component Analysis and Grey Relational Analysis. The proposed method consists of three stages. First, during preprocessing, we performed a Discrete Wavelet Transformation for data compression. Second, using Principal Component Analysis to project the input images into a low dimension subspace, we calculated the representative eigenface. Finally, we used Grey Relational Analysis to recognize the face images.
    To confirm our proposed method, we performed static and dynamic recognition experiments for analysis and comparison. ORL face databases were used in the static recognition experiments. Our database contained 40 people, and for each person, we selected 5 training samples. Using these training samples, we obtained an accuracy rate of 91.6 percent. In dynamic recognition experiments, we were able to obtain greater than 80 percent accuracy for 30 people under different distances.

    摘要……………………………………………………………………….I Abstract…………………………………………………………………II 目錄………………………………………………………………………III 圖目錄……………………………………………………………………IV 表目錄……………………………………………………………………VI 第一章 緒論……………………………………………………………1 1-1 前言…………………………………………………………………1 1-2 研究動機……………………………………………………………2 1-3論文架構………………………………………………………………4 第二章 人臉辨識的相關研究…………………………………………7 2-1以整體外觀為主的方法……………………………………………7 2-2以樣板比對為主的方法…………..……………………………11 2-3以階層式影像分析為主的方法…………………………………13 2-4以幾何分析為主的方法..…………………………………………14 第三章人臉偵測和人臉辨識方法……………………………………17 3-1膚色分割..………………………………………………………18 3-2去除雜訊…………………………………………………………21 3-3取得連通成分和標記人臉候選區………………………………25 3-4眼睛和嘴唇的偵測…………………………………………………29 3-5幾何判斷…………………………………………………………35 第四章人臉辨識方法 4-1離散小波轉換…………………………………………………………37 4-2主成份分析法………………………………………………………39 4-3灰關聯分析法……………………………………………………41 第五章 實驗與分析………………………………………………………43 4-1動態人臉偵測………………………………………………………43 4-2靜態人臉辨識.....................................51 4-3動態人臉辨識…………………………………………………………57 第六章 結論…………………………………………………………………63 參考文獻………………………………………………………………………64

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    其他參考資料
    人體計測資料庫,http://www.iosh.gov.tw/ergo.htm。
    鐘國亮, “ Image Processing and Computer Vision ”東華書局股份有限公司,2004

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