臺灣博碩士論文加值系統

在影像中偵測人臉的困難度來自於人臉的pose、臉部表情、人臉是否被遮蔽、以及光源的情況。我們提出了一個方法從靜態影像中偵測各種pose的人臉。我們提出的方法包含三個步驟。第一個步驟，使用膚色模型偵測出膚色的像素，再用相鄰元素分析找出膚色區域的位置。第二個步驟，先把膚色區域轉換成灰階影像，使用邊緣偵測演算法找出膚色區域的線條，並計算出膚色區域的特徵向量。我們提出的特徵向量包含兩個部份。第一個部分，將完成邊緣偵測的影像切割成3*4的格子，計算每個格子的水平線及垂直線的個數。第二個部份，計算邊緣影像的顏色相關直方圖的總和。第三個步驟，萃取一組訓練資料的特徵向量，並且用模糊群集法找出屬於人臉的群集。如果膚色區域的特徵向量與人臉群集的距離小於門檻值，該區域則判斷為人臉。實驗結果顯示出，我們的方法可以處理pose、旋轉、大小等變異。

The challenges for face detection from images come from the variation of poses, facial expressions, occlusions, lighting conditions, and so on. We propose a method for multiple-pose face detection from still images. Our proposed method consists of three phases. First, skin pixels are extracted using a skin color model. Connected component analysis is performed to find the skin regions. Second, before extracting the feature vector of a skin region, we apply edge detection to the region. Our feature vector consists of two parts. The first part is obtained by dividing the edge image into 3*4 grids and calculating the number of horizontal edges and the number of vertical edges in each grid. The other part is obtained by computing the summary of color correlogram of the edge image. Third, with a set of training images, the fuzzy c-means (FCM) clustering algorithm is used to build face models. If the Euclidian distance between a feature vector and a face model does not exceed a predefined threshold, the region will be classified to a face. The experimental results show that our method can deal with the variation in poses, rotations, scales, and so on.

Table of Contents i
List of Figures ii
List of Tables iii
Chapter 1 Introduction 1
Chapter 2 Literature Survey 3
2.1 Knowledge-based methods 3
2.2 Feature invariant methods 4
2.3 Template matching methods 5
2.4 Appearance-based method 6
2.5 Discussion 11
Chapter 3 Fuzzy C-means Clustering 13
Chapter 4 Multiple-pose Face Detection 15
4.1 Skin pixel detection 15
4.2 Feature vector extraction 18
4.2.1 Image enhancement 19
4.2.2 Edge detection 20
4.2.3 Feature vector analysis 21
4.3 Building face models using FCM clustering 22
4.4 Classification 23
Chapter 5 Experiment and Performance Evaluation 24
5.1 Performance evaluation 25
5.2 Comparison with other systems 28
Chapter 6 Conclusion and Future Work 30
References 32

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