博碩士論文 93532003 詳細資訊




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姓名 楊煒達(Wei-da Yang)  查詢紙本館藏   畢業系所 資訊工程學系在職專班
論文名稱 簡易方法之少量人臉辨識系統
(A simple approach to a small-scaled face recognition system)
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摘要(中) 人臉辨識可以廣泛的應用在各個不同的領域之中,比方大樓門禁管制、犯罪人員辨識、金融提款的安全驗證,以及網路交易的身份識別等,都可以使用,不僅可靠,而且快速方便。
本論文使用結合PCA與臉部特徵的人臉辨識,其中包含使用PCA-based求出Eigenvalue、Eigenvector和Eigenspace,再藉由投影,得到輸入Sample與各訓練人臉的距離。此外,還藉由投影和邊緣偵測求得雙眼和嘴巴三個區域,以及雙眼、眼角和嘴巴七個特徵點,最後做正規化和特徵區域比對,然後與訓練之人臉做辨識。
結果驗證部分,本實驗使用Cohn Kanade Database,裡面包含了九十七位不同的人,每人各選十張,分成九組,其辨識率皆為100%。並且另外自行拍攝十個人,共六十張照片,來做訓練和辨識之用,其結果也皆可達到100%。
摘要(英) The Face recognition can be applied to many different fields. For example, the building entrance guard control, the criminal verification, the security verification of finance, and the identity verification of the network trade, etc. The face recognition is not only reliable but also fast and convenient.
This disquisition combines PCA with face features for face recognition, including using PCA-based to figure out the Eigenvalue, Eigenvector and Eigenspace. Then get the distance between sample image and training image by the projection. Besides, we can get the eye and mouth areas, and the feature points of the eyes, canthi and the mouth by using the projection and edge detection. And finally, we do the normalization and area comparison to recognize the input image.
The result verification. we use the Cohn Kanade Database that includes ninety-seven different persons. Ten pictures each person and we divide them into nine sets. All of the recognition rates are 100%. In addition, we make a face image database by ourselves. That includes ten different persons and sixty pictures to do the training and recognition. The recognition rate is also 100%
關鍵字(中) ★ 人臉辨識
★ 特徵空間投影
★ 特徵空間
關鍵字(英) ★ eigenvector
★ face recognition
★ PCA
★ eigenvalue
★ eigenspace
論文目次 摘要 i
Abstract ii
目錄 v
圖目錄 viii
表目錄 x
第一章 緒論1
1.1 研究動機1
1.2 研究目的2
1.3 論文架構3
第二章 人臉辨識介紹與相關研究之探討4
2.1 人臉辨識介紹4
2.2 人臉偵測9
2.2.1 方法介紹11
2.3 PCA介紹14
2.3.1 Eigenvalue and Eigenvector18
2.3.2 特徵空間19
2.3.3 特徵空間投影20
2.3.4 PCA之缺點21
2.4 OpenCV介紹23
第三章 訓練與辨識之方法與步驟25
3.1 人臉特徵區域擷取(幾何特徵)25
3.1.1 特徵區域擷取25
3.1.2 特徵區域最佳化26
3.1.2.1灰階轉換26
3.1.2.2二值化處理27
3.1.2.3特徵區域最佳化27
3.2 人臉特徵點擷取(幾何特徵)32
3.2.1 眼角特徵點擷取32
3.2.2 眼睛特徵點擷取34
3.2.3 嘴巴特徵點擷取37
3.3 特徵區域比對(幾何特徵)39
3.4 PCA特徵擷取(統計特徵)39
3.5 人臉辨識系統架構40
3.6 訓練方法及步驟42
3.7 辨識方法及步驟43
3.7.1 辨識結果統計方法43
3.7.2 辨識流程45
第四章 實驗結果與分析47
4.1 人臉資料庫47
4.1.1 ORL人臉資料庫47
4.1.2 中研院人臉資料庫48
4.1.3 Yale人臉資料庫48
4.1.4 Stirling人臉資料庫49
4.1.5 Cohn Kanade人臉資料庫49
4.1.6 自製人臉資料庫50
4.2 實驗環境50
4.3 相關實驗辨識結果52
4.3.1 Face recognition committee machine52
4.3.2 Robust face recognition using minimax
probability machine54
4.3.2 A Method For Improved PCA in Face Recogntion 55
4.4 本實驗辨識結果55
第五章 結論與未來展望58
5.1 結論59
5.2 未來展望60
參考文獻 61
附錄一 ORL人臉資料庫部分人臉資料68
附錄二 中研院人臉資料庫部分人臉資料69
附錄三 Yale人臉資料庫部分人臉資料70
附錄四 Stirling人臉資料庫部分人臉資料71
附錄五 Cohn Kanade人臉資料庫部分人臉資料72
附錄六 自製人臉資料庫部分人臉資料73
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指導教授 蘇木春(Mu-chun Su) 審核日期 2007-7-5
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