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研究生:陳建志
研究生(外文):Chien-chih Chen
論文名稱:利用統計方法從影像中自動偵測人臉特徵點
論文名稱(外文):A statistical Approach to Automatic Facial Feature Extraction from Images
指導教授:賴尚宏
指導教授(外文):Shang-Hong Lai
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:75
中文關鍵詞:人臉偵測人臉特徵點偵測主成份分析
外文關鍵詞:Face detectionFacial feature extractionPrincipal Component Analysis
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在這篇論文中,我們提出了一個自動從影像當中找出人臉,並且準確偵測人臉特徵點的方法.利用大量的人臉影像訓練資料,我們可以得到一些統計的資訊:包括了人臉樣板(face templates),人臉特徵點影像的統計模型,以及人臉特徵點相對位置的統計模型.本篇論文包含了兩個主要部分:人臉偵測演算法,以及人臉特徵點選取演算法.
在人臉偵測演算法中,我們先將人臉影像的訓練資料分成幾個類別,並求取每個類別的平均值以得到人臉樣板.然後將求得的這些人臉樣板經過一些2D幾何轉換產生出大量用來搜尋比對人臉的影像資料.我們利用這些影像之間彼此的相似度建立一個階層式的架構,如此測試影像便不需與全部的影像資料比對而達到加速比對的目的.
這篇論文的第二部分是偵測人臉特徵點的演算法,我們所設定要尋找的人臉特徵點包括眼角以及嘴角.透過主成份分析(principal component analysis ,PCA)技巧的運用,可以得到特徵點影像以及人臉特徵點分佈位置的統計模型.我們會先在每個特徵點各自的小範圍搜尋區域內找出最類似眼角(或嘴角)的幾個候選點,然後分析這些特徵候選點的組合是否為合理的人臉特徵點的分佈位置.最後再以權值分析(weighted sum)的方式考慮特徵點影像的相似度以及分佈位置的合理性兩項因素來決定最佳的一組解.

In this thesis, we propose a novel algorithm to automatically extract facial feature points from image. Our approach is base on the statistical models computed from a large number of training images. There are two parts in this thesis, namely face detection and facial feature points extraction. To extract facial features from images, we have to locate the human face first. Our face detection algorithm is based on the multi-templates matching method. The face templates are computed from the collected face images. A hierarchical nearest neighbor network is applied to store the transformed face templates and it can accelerate the matching process between the test image blocks and face templates.
The second part in this thesis is the facial feature points extraction, including the corners of eyes and the corners of mouth. We apply the technique of principal component analysis (PCA) to calculate the statistical models for the feature images and facial configurations. We first locate several candidate feature points in each local area on face. Then we consider each combination of these candidate feature points to see whether it is a reasonable facial configuration. Finally, we use the weighted sum of image similarity measure and face shape approximation error to determine the best combination of the candidate feature points.

CHAPTER 1. INTRODUCTION
CHAPTER 2. REVIEW OF PREVIOUS WORKS
2.1 RELATED WORKS OF FACE DETECTION
2.2 RELATED WORKS OF FACIAL FEATURE EXTRACTION
CHAPTER 3. FACE DETECTION ALGORITHM
3.1 TRAINING PHASE
3.1.1 Getting the training face data
3.1.2 Estimating face features
3.1.3 Classifying the feature vector
3.1.4 Hierarchical Nearest Neighbor Network For Face Image
3.1.5 Hierarchical Nearest Neighbor Network For Non-Face
Examples
3.2 EXECUTION PHASE
CHAPTER 4. FACIAL FEATURES EXTRACTION ALGORITHM
4.1 TRAINING PHASE
4.1.1 Construction of the statistical face shape model
4.1.2 Constructing the local facial feature templates
4.2 EXECUTION PHASE
CHAPTER 5. EXPERIMENTAL RESULTS FOR FACE DETECTION ALGORITHM
CHAPTER 6. EXPERIMENTAL RESULTS FOR FACIAL FEATURE EXTRACTION
ALGORITHM
CHAPTER 7. CONCLUSION
REFERENCES

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