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研究生:戴郁文
研究生(外文):Tai Yuwen
論文名稱:控制環境下的人臉特徵辨識
論文名稱(外文):A Study of Facial Feature Recognition in Controlled Environments
指導教授:王聖智王聖智引用關係
指導教授(外文):Sheng-Jyh Wang
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電子工程系
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:2002
畢業學年度:90
語文別:中文
論文頁數:120
中文關鍵詞:人臉特徵辨識
外文關鍵詞:facial feature recognition
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在本論文當中,提出了一套全自動的人臉特徵辨識法,可針對在控制環境下拍攝的人臉影像,自動標示出人臉特徵點與特徵輪廓線的位置。在我們的演算法中,將人臉特徵辨識的問題分為兩個階段處理,在初始猜測的階段,藉由模糊理論來大略辨識出嘴巴區塊、眼睛區塊、眉毛區塊、下巴區塊和鼻孔區塊,並依據這個大略辨識的結果給予人臉特徵點一組較佳的初始位置;在人臉特徵辨識的階段,我們針對不同的人臉特徵設計了幾組適合用來辨識人臉特徵的主動輪廓模型(active contour model,又稱snake),我們所設計的主動輪廓模型不只使主動輪廓模型本身那條主動曲線能描繪出人臉當中的部份特徵輪廓線,其中一部份用來定義主動輪廓模型的元素(snaxel)更能直接對應到MPEG-4中定義的人臉定義參數(Facial Definition Parameter, FDP),標示出人臉當中的重要特徵點。根據實驗結果顯示,我們的演算法能夠相當準確地標示出在控制環境下拍攝的人臉影像中部份特徵點和特徵輪廓線位置(包含嘴巴、眼睛、和人臉輪廓)。

In this thesis, we propose a new approach of facial feature recognition which can automatically locate specific facial feature points and contours in a 2D color image, which is captured in controlled environments. In our approach, we do facial feature recognition in two stages: first recognize facial features (mouth region, eye regions, eyebrow regions, chin region and nostrils regions) approximately by fuzzy logic. According to this approximate recognition of facial features, we give a good initial guess of facial feature points. Then, for different facial features, we use different active contours to extract more accurate facial feature points and contours. Our modified active contour is especially designed for facial feature recognition. Not only the active contours themselves may well represent facial feature contours, but some of the snaxels also correspond to FDP parameters in MPEG-4 to locate facial feature points directly. According to our simulation results, our approach can locate specific facial feature points and contours (mouth, eyes and facial contour) in a 2D color image with reasonable accuracy.

目錄
Chapter 1 緒論 1
1.1 前言 1
1.2 系統概觀 3
1.3 摘要 5
Chapter 2背景資料 6
2.1 人臉特徵辨識相關研究 6
2.1.1 以特徵為基礎的辨識法 6
2.1.2 以模型為基礎的辨識法 7
2.1.2.1 樣版比對 (Template Matching) 8
2.1.2.2 變形樣版(Deformable Template) 9
2.1.2.3 主動形狀模型(Active Shape Model, ASM) 11
2.2 MPEG-4人臉模型 14
2.2.1 MPEG-4標準 14
2.2.2 MPEG-4人臉動畫的基本說明 15
2.2.3 IST三維人臉模型 16
2.3 Fisher線性判別函數(FLD) 17
2.4 色彩空間 22
2.4.1 RGB 色彩空間 23
2.4.2 CIEXYZ色彩 24
2.4.3 CIELAB 色彩空間 27
2.5 模糊邏輯(Fuzzy Logic) 29
2.5.1 模糊集合(Fuzzy Set) 29
2.5.2 規屬函數(Membership Function) 30
2.6 主動輪廓模型(Active Contour Model, 又稱Snake) 31
2.6.1 主動曲線(Active Curve) 31
2.6.2 能量函數(Energy Function) 33
2.6.3 能量極小化(Energy Minimization) 33
Chapter 3 初始猜測 35
3.1 資料庫 35
3.1.1 XM2VTS 人臉影像資料庫 35
3.1.2 訓練資料集 36
3.2 人臉偵測 37
3.2.1 膚色分佈統計特性 37
3.2.2 皮膚區域選取 38
3.2.2.1 初始選取膚色區域 38
3.2.2.2 重新選取膚色區域 39
3.2.3 人臉和形狀特徵的偵測 39
3.3 人臉特徵候選區塊 43
3.3.1 選取人臉特徵候選區塊 43
3.3.2 幾何形狀特徵 46
3.3.3 Fisher 線性判別函數(FLD) 49
3.3.3.1 膚色和唇色群組的Fisher線性判別函數 50
3.3.3.2 膚色和臉部毛髮色群組的Fisher線性判別函數 52
3.4 辨識人臉特徵候選區塊 54
3.4.1 嘴巴區域 55
3.4.2 眼睛和眉毛區域 58
3.4.3 下巴區域 63
3.4.4 鼻孔區域 63
3.5 人臉特徵點的初始位置 64
Chapter 4 人臉特徵辨識 72
4.1 嘴巴區域的人臉特徵 74
4.1.1 嘴唇外緣 74
4.1.2 張開時的嘴巴內緣輪廓 80
4.1.3 嘴巴閉著時的嘴唇內緣輪廓 85
4.1.4 實驗結果 88
4.2 人臉輪廓上的特徵 91
4.3 眼睛區域的人臉特徵 99
4.4 IST 人臉三維模型 107
Chapter 5 結論 111
附件 A 112
參考文獻 116

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