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研究生:吳建樺
研究生(外文):Jimmy Wu
論文名稱:利用多類支持向量機混合三角偵測於人臉辨識之研究
論文名稱(外文):Face Recognition using Multi-class Support Vector Machines mixture Triangle-based approach
指導教授:林群雄林群雄引用關係
指導教授(外文):Lin C.S.
學位類別:碩士
校院名稱:國立臺北大學
系所名稱:資訊管理研究所
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:2006
畢業學年度:94
語文別:中文
論文頁數:53
中文關鍵詞:支持向量機Triangle-based approach人臉偵測人臉辨識
相關次數:
  • 被引用被引用:2
  • 點閱點閱:224
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
人臉辨識研究進行已有近三十年之久,而在近十年中,人臉辨識以幾種核心技術使得辨識能力有顯著的進步。例如:以特徵為基礎、以模版比對為基礎與以類神經網路為基礎的辨識方法。由於以往的識別方式會因為環境的變化而造成辨識的失誤,並使速度大大的下降。近年來的研究除了專注於辨識速度上的提升之外,也盡力解決各種環境所帶來的影響,如光線、陰影、人臉角度及表情變化等等。
支持向量機(Support Vector Machines) 是在統計學習理論的基礎上發展起來的新一代學習演算法,該演算法在文本分類、手寫識別、圖像分類、生物資訊學等領域中獲得了良好的應用,為近年來非常熱門的一項技術。在本篇論文中,我們使用了三角偵測法(Triangle-based approach)來判斷一張圖像人臉所在之位置,在取得雙眼以及嘴巴所形成之等腰三角形後,利用雙眼間之距離,將所需要的人臉範圍切割出來,做為人臉辨識的訓練資料。並使用多類支援向量機(Multi-Class SVM),來做為執行人臉辨識的核心技術。我們將實驗利用二種資料庫來做測試。實驗一使用自建資料庫,其中包含30個人,每人20張人臉,共600張圖像的資料。實驗二使用CMU PIE的人臉資料庫,其中包含68個人,取其正面部份,每人共60張,總共4080張圖像。實驗一的結果顯示SVM與PCA在辨識同樣資料時,SVM可以達到90.83%,而PCA只有77.68%。實驗二的結果顯示SVM在處理CMU PIE資料庫時,對於表情的變化依然可以成功的辨識,辨識率可達到94.0441%。
Face recognition has researched for thirty years, in recent ten year, face recognition improved with few kernel technology. Example: Feature-based、Template-based、Neural Network-based. In the past, recognition rate will changed with environment changes, and slow down the recognition speed. Recent researches focus on accuracy and speed of recognition, except that, the researches focus on environment changes too. Example: Light、illuminations、angle of human face and etc.
Support Vector Machines is a new generation algorithm based on Statistical Learning Theory (STL). Support Vector Machines is a popular technology and applies in many domains. It applies in texture classification、signature recognition、image classification、bioinformatics and etc. In this paper, we use Triangle-based approach to detect face region in a graphic, retrieve eyes and mouth’s position that is a isosceles triangle, and crop face region based on two eyes’ distance. The crop face region is the training data we need. The training data will transfer to multi-support vector machines, the SVM is our kernel technology for face recognition. We use to database to testing. The test 1, we use database created by ourselves, it’s includes 30 people and each people have 20 images, totally for 600 images. In test 2, we use CMU PIE database, includes 68 people, each people have 60 images, totally for 4080 images. Result in test 1 shows the same data for SVM and PCA, accuracy of SVM is 90.83%, but PCA only have 77.68%. In test 2 shows accuracy of SVM is 94.0441% in CMU PIE database, and can also recognition for facial expression changes.
壹、 簡介 4
貳、 文獻探討 5
一、 人臉偵測 5
(一) 使用特徵的方式 (Feature Invariant Approaches) 5
(二) 使用範本比對的方式 (Template Matching Methods) 7
(三) 使用外觀為基礎之方法(Appearance-based methods) 7
二、 人臉辨識 9
(一) 以擷取特徵為基礎之方法(Feature-based) 9
(二) 以範本配對為基礎之方法(Template Matching) 10
(三) 以類神經網路為基礎之方法(Neural Network) 10
(四) 以主要成分分析為基礎之方法(Principal Component Analysis) 10
參、 支持向量機原理 12
一、 線性支持向量機(Linear Support Vector Machines) 13
(一) 統計學習理論 13
(二) VC維原理(Vapnik Chervonenkis Dimension) 14
(三) 支持向量機 15
二、 非線性支持向量機(Non-Linear Support Vector Machines) 20
(一) 特徵空間 20
(二) Kernel函數(Kernel Function) 21
三、 多類支持向量機(Multi-class Support Vector Machines) 25
(一) 一對一為主的方式(One-against-one Method) 25
(二) 一對多為主的方式(One-against-all Method) 26
(三) 有向非循環圖形方法(Directed Acyclic Graph Method) 26
(四) C&S Method 27
肆、 實驗結果與討論 28
一、 輸入圖像 28
二、 使用三角偵測取得人臉 30
三、 人臉辨識實驗結果 33
(一) 第一組人臉資料庫實驗結果:使用自建資料庫做測試 33
(二) 第二組人臉資料庫實驗結果:使用CMU PIE Database做測試 39
伍、 結論與未來展望 47
陸、 參考文獻 49
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