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研究生:郭朝易
研究生(外文):Chao-Yi Kuo
論文名稱:使用向量坐標進行臉部辨識之研究
論文名稱(外文):A Study of Face Recognition Using Coordinate Vector
指導教授:陳恩航陳恩航引用關係
指導教授(外文):An-Hang Chen
學位類別:碩士
校院名稱:國立臺北商業技術學院
系所名稱:資訊與決策科學研究所
學門:電算機學門
學類:電算機應用學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:51
中文關鍵詞:生物辨識技術坐標向量卡方分配齊一性檢定行動載具
外文關鍵詞:biometrics technologiescoordinate vectorschi-squarehomogeneitymobile technology
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生物辨識技術(biometrics technology)的領域內,有針對五官、語音、指紋、虹膜等方面作辨識的用途,然而本論文主題只針對臉部辨識作相關研究。我們希望能用簡單的辨識原理來找出辨識的依據,因此採用坐標向量(coordinate vectors)的概念,利用線段與角度的關係來找出規則性,最後採用統計上的卡方分配(chi-square)作齊一性(homogeneity)檢定,檢定出是否接受與拒絕此虛無假設,當然我們也希望此方法可以快速、有效率找出真正對象,另外更希望可以結合在行動載具(mobile technology)中,讓它當作一種具有關鍵性、服務性的工具。
In the field of biometrics technology, there are many methods for recognizable purposes such as facial features, voices, fingerprints, iris, etal. The topic of this thesis is only used for face recognition research. But we figure out a simple identifiable principle. Our concept is using coordinate vectors and utilizes the relationship between lines and angles to find the regularity. Finally, we adopt the chi-square distribution for homogeneity verification to verify whether accept or reject the null hypothesis(two faces are same). We expect this method can be quick and efficient to find the different faces. On the other hand, we also hope this method can be combined with the mobile technology as critical and service tools.
目錄

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第1章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 5
1.4 研究範圍與限制 6
1.5 研究流程 7
1.6 章節結構 9
第2章 人臉辨識介紹與相關研究之回顧 10
2.1 以特徵為基礎 12
2.1.1 色彩空間 12
2.1.2 膚色分割 14
2.1.3 二值化 15
2.1.4 影像形態學處理 16
2.1.5 人臉區域判定方法 16
2.2 以學習為基礎 18
2.2.1 矩形特徵 19
2.2.2 積分影像 21
2.2.3 瀑布模型及可變子視窗 22
第3章 研究辨識方法之規則 25
3.1 探索人臉偵測軟體 25
3.1.1 Web Services 25
3.1.2 Free Ware 26
3.1.3 Commercial Ware 26
3.2 人臉的切割分佈 28
3.3 初階檢查項目 30
3.3.1 線段類別 30
3.3.2 角度類別 32
3.4 給定編號 34
3.5 核心KEY號碼 36
3.6 卡方分配-齊一性檢定 37
第4章 實驗數據與分析 38
4.1 實驗對象 38
  4.2 初階分析階段 41
4.3 進階分析階段 44
4.4 實驗結果 46
第5章 結論與未來研究方向 47
5.1 結論 47
5.2 未來研究方向 47
參考文獻 48


表目錄

1 各種生物辨識技術比較表................................................ 3
2 人臉偵測方法的分類表................................................ 18
3 矩形特徵計算方式................................................ 20
4 用於Web Service之人臉偵測的範疇.............................................. 25
5 用於Free Ware之人臉偵測的範疇................................................ 26
6 用於Commercial Ware之人臉偵測的範疇...................................... 26
7 線段檢查項表的相對比例值................................................ 32
8 角度檢查項表之比例值................................................ 33
9 檢查項目表內,各組編號的組距範圍................................................ 35
10 KEY之純量與組距編碼................................................ 36
11 第一次收集實驗對象的純量與編號(組距編號)…....................... 39
12 第二次收集實驗對象的純量與編號(組距編號)…....................... 40
13 初階篩選之查表得知編號................................................ 41
14 初階篩選之組距編號合併而成之KEY值......................................... 42
15 初階篩選之查表得知編號與KEY值差異表..................................... 43
16 線段與夾角之原始數據............................................... 44
17 1對多的方式,卡方值與結果分析............................................... 45
18 實驗結果之比較表................................................ 46


圖目錄

1 目前生物辨識核心技術比率的分佈情形...............…..................... 2
2 FaceSDK臉部偵測流程............................................... 7
3 研究臉部辨識的流程............................................... 8
4 RGB色彩空間座標圖........................................... 13
5 四連通、八連通的區域示意圖........................................... 17
6 四連通、八連通的方向示意圖........................................... 17
7 弱分類器的架構雛型........................................... 21
8 計算積分影像概念........................................... 22
9 Haar-like 特徵區域的示意圖........................................... 22
10 瀑布模型內之子視窗示意圖........................................... 23
11 瀑布(Cascade)模型流程圖........................................... 24
12 FaceSDK特徵點之展示........................................... 27
13 收集人臉樣本之系統介面........................................... 27
14 進行實驗時對焦之情況........................................... 27
15 進行實驗時未對焦之情況........................................... 27
16 東方人指的三庭五眼示意圖........................................... 28
17 臉長佔各部位百分比........................................... 29
18 臉寬佔各部位百分比........................................... 29
19 Leslie G. Farkas所提出的臉部長度計算....................................... 30
20 水平與垂直之各項部位長度.......................................... 31
21  X軸、Y軸之間的夾角圖........................................... 32
22 檢查項目之特徵夾角示意圖........................................... 33
23 距離遠近時,同一張臉孔角度維持不變..................................... 33
24 正負3個標準差之常態分配示意圖........................................... 34
25 α = 0.05的信賴區間........................................... 37



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