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研究生:莊啟鴻
研究生(外文):Chuang, Chihung
論文名稱:使用五官特徵之人臉辨識
論文名稱(外文):Face Recognition Using Facial Features
指導教授:蔡吉昌蔡吉昌引用關係
指導教授(外文):Tasi, Jyichang
口試委員:蔡吉昌危永中吳靖純
口試委員(外文):Tasi, JyichangWei, YungchungWu, Chinchun
口試日期:2012-06-22
學位類別:碩士
校院名稱:義守大學
系所名稱:資訊管理學系碩士在職專班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:52
中文關鍵詞:主成分分析小波轉換人臉辨識形態學特徵空間
外文關鍵詞:Principal Component AnalysisWavelet TransformFace RecognitionMorphologyEigen Space
相關次數:
  • 被引用被引用:9
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擷取五官特徵來辨識人臉是人臉辨識常用的方法之一,它的辨識過程主要是利用人臉五官的相對大小及形狀的特徵來做辨識,然如何選取特徵及特徵的數量選擇是五官特徵人臉辨識是否成功的關鍵。本論文提出一套新的人臉五官特徵表示及特徵的數量選擇,除對於五官的大小採取相對的比例外,在眼睛及嘴唇的外型上,論文提出以二次曲線來近似。研究中,初期共選取32個特徵,為了從32個特徵中選取有效的特徵組合,論文是以各式的特徵組合來做測試,並篩選出辨識率最高的一組特徵為人臉最終應擷取的特徵組合,然32個特徵組合是一個非常大的數字,因此為了快速決定哪組特徵組合是最佳組合,論文提出一套候選影像選取法則,此選取法則主要是根據每個特徵組合的候選影像來做統計,依統計的結果來決定辨識的結果,由於這種利用統計的選取法則是架構在此假設之下:「一個有效的特徵在其少數的候選影像中應包含正確的結果」,所以每一個特徵只要選取少數的候選影像即可,如此可加快處理的時間。經實驗結果顯示,本論文所提的方法可達到86.2%的辨識率。
Extracting human facial features is one of schemes often used in the field of face recognition. The face recognition using facial features primarily utilizes the relative size of facial organs, such as eyes and mouth, and face shape to identify one or more persons by comparing it with faces stored in a database. However, how to select facial features and the number of facial features is a key point of face recognition. The thesis proposes a new set of facial features; besides using the features of the relative size of facial organs, the features of the shape of eyes and mouth approximated by Quadratic curves also are included. In the initial stage of the study, each face is extracted thirty-two facial features; then, grouping the thirty-two facial features for finding the best combination which has the highest recognition rate is executed. However, the number of combination is very large; therefore, for resolving the problem, the study presents a rule to select candidate images of each feature by statistic and decide the result from the candidate images. The rule is based on the assumption: an effective feature should contain the correct result in a small number of candidate images. So, each feature just selects several candidate images. This will be able to save a lot of time. The experimental results show that the proposed method can achieve 86.2% recognition rate.
摘要i
Abstract ii
目錄iii
圖目錄v
表目錄vii
第一章 緒論1
1.1 前言1
1.2 研究目的2
第二章 文獻探討3
2.1 人臉偵測相關研究4
2.2 人臉辨識相關研究5
第三章 人臉偵測6
3.1 人臉膚色分析7
3.1.1 色彩空間轉換7
3.1.2 膚色分割10
3.2 人臉定位13
3.2.1 雜色移除13
3.2.2 區域連通串列化16
3.2.3 臉部五官定位18
第四章 人臉辨識20
4.1 小波轉換21
4.2 主成分分析24
4.2.1 主成分分析原理24
4.2.2 主成分分析架構之人臉辨識27
第五章 研究方法31
5.1 人臉辨識決策準則32
5.2 建立人臉五官資料庫與人臉偵測33
5.3 建立PCA人臉資料庫與人臉辨識33
第六章 實驗結果與討論34
第七章 結論與未來展望36
7.1 結論36
7.2 未來展望37
文獻參考37
圖目錄
圖3.1 一般常用的人臉快速偵測前置處理之流程7
圖3.2 HSV色彩模型10
圖3.3 正規化膚色分佈圖13
圖3.4 光的原色和次色彩13
圖3.5 原始圖像13
圖3.6 膚色分析結果14
圖3.7(a) 膚色二值化矩陣15
圖3.7(b) 遮罩矩陣15
圖3.8 影像I被結構元素P侵蝕16
圖3.9 影像I被結構元素P膨脹16
圖3.10 二值化影像17
圖3.11 8連通與4連通18
圖3.12 二值化影像與8連通標記18
圖3.13 連接元區域標定結果19
圖3.14 嘴唇範圍定位19
圖3.15 眼睛範圍定位20
圖3.16 擷取人臉21
圖4.1 PCA人臉辨識與建立人臉資料庫22
圖4.2 小波轉換架構圖24
圖4.3 執行1、2階DWT後所對應的頻帶圖25
圖4.4 原始灰階影像,分別經過1階、2階小波轉換後的結果25
圖4.5 中研院資料庫取前27大特徵值之特徵臉影像29
圖4.6 人臉特徵示意圖33
圖6.1 部份的訓練影像37
表目錄
表6.1 PCA實驗結果37
表6.2 五官特徵實驗結果37
中文部份
[1]李建興、林應璞、游凱倫,"即時人臉偵測與辨識",技術學刊,Vol. 24, No. 2, pp. 131-141,2009。
[2]李棟良,梁振升,王於藩,陳家閔,曾鈺鈞,簡煒玲,人臉偵測與辨識系統,2008
[3]林晁立, "以臉部器官形狀、寬度、相對位置從事人臉影像辨識",東海大學-資訊科學學系,2000。
[4]林咸仁, "改良線性鑑別式分析在少量訓練樣本下之人臉辨識研究",國立成功大學-資訊工程學系,2002。
[5]徐毓洲,"應用機率類神經網路於人臉辨識之研究",高雄師範大學資訊教育研究所碩士論文,2007。
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[7]R. C. Gonzalez and R. E. Woods著,"數位影像處理",繆紹綱譯,台灣培生教育出版,台北,2004。
英文部份
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[10]J. Yang, D. Zhang, A. Frangi, and J.Y. Yang, "Two-Dimensional PCA:A New Approach to Appearance-Based Face Representation and Recognition" IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 26, no. 1, January 2004.
[11]K.K. Sung and T. Poggio, ªLearning Human Face Detection in Cluttered Scenes,º Computer Analysis of Image and Patterns, pp. 432-439, 1995.
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[14]M. Soriano, B. Martinkauppi, S. Huovinen, and M. Laaksonen, "Using the Skin Locus to Cope with Changing Illumination Conditions in Color-Based Face Tracking," Proc. IEEE Nordic Signal Processing Symposium, pp. 383-386, 2000.
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[20]S.-H. Jeng, H.- Y. M. Liao, C.- C. Han, M.- Y. Chern and Y.- T. Liu , "Facial Feature Detection Using Geometrical Face Model: An Efficient Approach, "Pattern Recognition, Vol. 31, No. 3, pp 273-282,1998
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[23]T. K. Leung, M. C. Burl, and P. Perona, "Finding Faces in Cluttered Scenes Using Random Labeled Graph Matching," Proceedings of 1995 IEEE International Conference on Computer Vision, pp. 637-644, 1995.
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[26]Web. Data of Center for Advanced Studies, Research and Development in Sardinia, "CCIR Recommendation 601, " http://www.crs4.it/OLD/LUIGI/MPEG/ccir601.html.
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