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研究生:侯建州
研究生(外文):Chien-ChouHou
論文名稱:各種人臉偵測方法之調查
論文名稱(外文):A Survey of Different Face Detection Methods
指導教授:連震杰
指導教授(外文):Jenn-Jier Lien
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
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:2012
畢業學年度:100
語文別:英文
論文頁數:57
中文關鍵詞:人臉偵測多角度人臉偵測基於外觀的方法AdaBoostVector BoostingWidth-First-Search (WFS) Tree
外文關鍵詞:Face DetectionMulti-view Face DetectionAppearance-based ApproachAdaBoostVector BoostingWidth-First-Search (WFS) Tree
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人臉偵測作為人與電腦交流(human-computer interaction)的第一步,目的是要從一張圖片或影像中找出人臉確切的位置,進而能拿來作為其他用途。找出圖上的人臉位置對人類來說是理所當然的事情,可是對電腦來說卻是一項挑戰,於是到現在為止已經有無數的研究在這一方面。本篇論文中會探討各種方法,並且分析各方面的優缺點,並選出優秀的方法來進行實作。將所有的方法區分成四大類型,從大的範圍縮小到Appearance-based Approach 類型的方法,這是在這十年多來進步最多的方法。再進一步探討其中,然後集中探討Boosting類型的演算法,最後從裡面選出Vector Boosting實作其演算法,並且進行改良的研究。本篇論文對Vector Boosting提出的改良方法可以非常有效的節省訓練所需要花的時間,而不會減低測試圖片的效能。
As the first step of human-computer interaction, face detection is a research topic in recent years. The subject is to finding face position in an image or video, and then we can use the as for other purposes. Finding a face on an image is a matter of course for people, but it is a challenge to computers. Until now, there have been numerous studies in this field. In this paper, we will survey different face detection methods, and the advantages and disadvantages of these methods would be analyzed. Finally we will choose an excellent methods and implementation the algorithm. All of the face detection methods can be divided into four categories, and then the range narrowed down to Appearance-based Approach, which is the most improved in this decade. The next step analyzes on Appearance-based Approach, and focus on boosting algorithm. Eventually, we choose vector boosting and implement its algorithm, try to find a way to improve it. We come up to a way that can save a lot of time in training, without lost the performance in testing.
摘要 I
Abstract II
誌謝 III
Contents IV
Figure Contents VII
Chapter 1. Introduction 1
1.1 Background 1
1.2 Motivation 1
1.3 Challenges of Face Detection 2
1.4 Paper structure 3
Chapter 2. Classification of Face Detection Approaches 5
2.1 Face detection 5
2.2 Knowledge-based approaches 5
2.3 Feature invariant approaches 7
2.4 Template matching approaches 8
2.5 Appearance-based approaches 9
2.6 Comparison and Analysis 12
Chapter 3. Feature Extraction Methods 14
3.1 Feature Extraction Methods 14
3.2 Haar-like Features 14
3.3 Composite Features 16
3.4 Statistics-based Features 18
3.5 Other features 19
3.6 Comparison and Analysis 20
Chapter 4. Learning Methods 22
4.1 Naive Bayes Classifier 22
4.2 Neural networks 22
4.3 Support vector machine (SVM) 24
4.4 Boosting 25
4.5 Comparison and Analysis 26
Chapter 5. Boosting Algorithm 28
5.1 Boosting Algorithm 28
5.2 Strong classifier structure 30
5.3 Comparison and Analysis 34
Chapter 6. Multi-View Face Detection Using Vector Boosting 37
6.1 Haar feature based LUT weak classifiers 37
6.2 Program process 38
6.3 Algorithm 40
6.4 Experimental results 43
6.4.1 Data Collection 44
6.4.2 Training Result 45
6.4.3 Discussion 47
Chapter 7. Conclusion and Future Work 53
Reference 54
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