臺灣博碩士論文加值系統

由於近年來人臉辨識已經廣泛被研究，而許多辨識的方法與理論也隨之而產生，但往往大部分的人臉辨識在人臉的角度上有著嚴格的限制，在本論文當中提供了虛擬的人臉系統以供多角度的辨識，只需一張正面的人臉，便可虛擬出不同角度的人臉，同時為了克服單張影像欠缺深度資訊的問題，所以加入了一般統計的方法去判斷臉型大概的深度，以減少在虛擬時誤差的產生，此識別系統主要包含人臉偵測、人臉虛擬以及人臉辨識三個系統。
人臉偵測系統中，利用膚色找到人臉的搜尋區域後，依橢圓樣板演算法，找出人臉的影像，再取得人臉上面各個特徵點資訊，進而模擬出三維的人臉，在擷取各角度的圖形做為判斷人臉的虛擬資料庫，最後以主分量分析（Principal Component Analysis，PCA）確認偵測到的影像是否為資料庫人臉影像。簡言之，本論文前段虛擬人臉系統可與其他的人臉辨識系統結合，並使判斷角度上有更大的彈性。

Face recognition system has been widely developed in recent years. Many methods and theories of recognition also have been created. However, most of face recognition systems involve strict constraints on angles of human faces. In this thesis, it will present a simulation system to provide face recognition from various angles. The system only requires the front side of a face and then , further , to simulate various angles of the face. Moreover, in order to overcome the issue that a single image lacks deeply detailed information, this system will apply statistic method to identify the approximate depth of the face to decrease the occurrence of deviation when simulating. The recognition system mainly includes face testing system, face simulation system and face recognition system.

In face testing system, it will utilize skin tone to find out the search area of faces. By ellipsoidal mask, images of faces can be identified and information of every characteristic can be obtained, further to simulate three-dimensional faces. These images with various angles can be captured to be used as a simulation database for face recognition. Finally, the system ensures whether the obtained images are face images in database by Principal Component Analysis technique. Briefly, this thesis will describe that the face simulation can be combined with other face recognition systems and achieve to higher elasticity in identifying angles.

目錄
中文摘要 I
Abstract II
致謝 III
目錄 IV
表目錄 V
圖目錄 VII
第一章 緒論 1
1.1前言 1
1.2研究動機與目的 2
1.3文獻探討 2
1.4論文架構 3
1.5研究成果與貢獻 4
第二章人臉偵測 5
2.1人臉選取判斷程序 5
2.1.1色彩分割與色彩空間介紹 6
2.2膚色檢測範圍 9
2.3雜訊移除 12
2.4連接元區域標定程序 14
2.5橢圓臉部搜尋程序 15
2.5.1 Sobel 濾波器邊緣擷取 15
2.5.2橢圓偵測 18
2.6眼睛偵測 20
第三章三維模型建構與選擇 23
3.1繪製一個三維模型 24
3.1.1 3Dmax建模 24
3.2模型移動與縮放 26
3.2.1物體平移(Translation) 26
3.2.2物體縮放(Scale) 27
3.2.3物體旋轉(Rotation) 29
3.3物體的投影 31
3.3.1垂直投影 31
3.3.2透視投影 32
3.4光線原理 36
3.5紋理對應 38
第四章 人臉驗證 40
4.1主成分分析法 40
4.1.1 PCA運算原理 40
4.1.2人臉樣本訓練 43
4.1.3以歐氏距離作為人臉辨識決策方法 46
第五章 實驗結果 47
5.1實驗環境與設備 47
5.2人臉偵測實驗結果 47
5.2.1膚色偵測 47
5.2.2人臉偵測 49
5.3人臉模擬實驗結果 52
5.3.1橢圓偵測與眼睛位置收尋 53
5.3.2虛擬實現 54
5.4人臉部辨識結果與討論 57
5.4.1驗證虛擬人臉資料庫 57
5.4.2即時辨識驗證 61
5.4.3辨識結果討論 63
第六章 結論與未來展望 64
6.1結論 64
6.2未來展望 64
参考文獻 65

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