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| 研究生： |
李俊達 |
|---|---|
| 論文名稱： |
彩色影像中的人臉偵測 Face detection in Color Image |
| 指導教授： | 何瑁鎧 |
| 學位類別： |
碩士
Master |
| 系所名稱： |
理學院 - 資訊科學系 |
| 論文出版年： | 2006 |
| 畢業學年度： | 95 |
| 語文別： | 英文 |
| 論文頁數： | 60 |
| 中文關鍵詞： | 色彩空間 、人臉偵測 、特徵 |
| 外文關鍵詞： | color space, face detection, haar-like feature |
| 相關次數： | 點閱：156 下載：103 |
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本論文的目的是利用人臉在彩色影像中所提供的多色彩空間資訊，來達成在變異度較大的光源中即時偵測人臉的任務。彩色影像所擁有的原始RGB色彩資訊，經過轉化到正規RGB以及HSV (色調、飽合、明度)等色彩空間後，擁有對光源變化反應減緩的特性。以此特性為基礎，在４個選定的色彩空間中定義８種不同的類赫爾特徵(Haar-like feature)，再利用推進演算法（Boosting algorithm）選出重要性最高的幾組特徵來進行對人臉的特徵。實驗結果顯示依此方法所產生的辨識器可在２點多秒內處理近百萬個次窗口(sub-window)，並對光源變化有相當程度的抵抗力。
The main goal of this thesis is to detect human face under varying lighting condition by utilizing multiple color space information in real-time. Images of RGB color space can be converted into normalized RGB and HSV color spaces and thus reduce the interference of lighting condition. Base on this mechanism, we define 8 Haar-like features inside 4 selected color spaces, and then select the important features with boosting algorithm. Experimental results show that detectors constructed with our approach are able to process nearly one million sub-windows within 2.4 seconds, being robust to the changes of lighting conditions.
CHAPTER 1 Introduction 1
1.1 Previous Detection Approaches 2
1.2 Proposed Approach 3
1.3 Organization of the Thesis 4
CHAPTER 2 Related Work 6
2.1 Haar-like Feature 6
2.2 Skin-Color Based Detection 6
2.3 Color Space 8
2.3.1 RGB 8
2.3.2 CMY and CMYK Color Space 9
2.3.3 HSV Color Space 11
2.3.4 Device-Independent Color Spaces 11
2.3.5 YUV YCrCb and YIQ Color Space 12
CHAPTER 3 Rectangle Feature 14
3.1 Feature Type 15
3.1.1 Haar-like Feature 15
3.1.2 DC Color Feature 16
3.2 Color Space 17
3.2.1 Normalized RGB Color Space 17
3.2.2 HSV Color Space 18
3.3 Proposed Feature 20
3.3.1 Rectangle Feature Set 20
3.3.2 Color Conversion 21
CHAPTER 4 Detection Framework 25
4.1 Weak Classification Function 25
4.2 AdaBoost Algorithm 27
4.3 Cascade Classifier 30
CHAPTER 5 System Framework 33
5.1 Rectangle Feature 34
5.2 Training Data 35
5.3 Weak Learner 36
5.4 Weak Classifier 36
5.5 Learning Algorithm 37
5.6 Strong Classifier 37
5.7 Cascade Classifier 38
CHAPTER 6 Experiment 40
6.1 Feature Definition 40
6.2 Implementation 41
6.2.1 Preprocessing 41
6.2.2 Cascade Construction 44
6.3 Detection Result 46
CHAPTER 7 Discussion And Conclusion 52
Reference 54
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