人臉角度估測是一個非常重要的技術, 人臉的角度在許多方面代表了很多意義. 它可以顯現出一個人目前對環境的人事物是否感興趣,也可以代表一個人目前專注在哪個方位. 為了估測人臉所在的方位, 人臉的特徵點必須能夠準確的追蹤, 所以追蹤這些特徵點的演算法就顯得特別的重要. 粒子濾波器是一個用於追蹤的演算法,他是另一種表現卡爾曼濾波器的一種型態, 它能利用目前所觀察到的數值來預估下一個時間的狀態. 在這篇論文中我們提出了一個模型粒子濾波器來改善典型的粒子濾波器,我們利用非線性迴歸分析來訓練狀態轉移模型,來提升粒子濾波器中狀態轉移函數的效率. 而從實驗中的數據也可比較出所提出的分法比典型粒子濾波器好.

Head pose estimation is a technique that determinate the orientation of face. The orientation of human face is a important information, face is a significant symbol that show human attention and behavior. For estimating the pose of head, tracking the feature points on face is very important. Particle filter is a tracking algorithm that alternative of extend Kalman filter, it has been widely used for solving tracking problem. It predict a moving object location from observation value that contains noises. In this paper, we propose a model-based particle filter that tracks the feature point on the face and fits by AAM. the proposed model-base particle filter that use non-linear regression analysis to train a state transition model to make the state transition more efficiently. The experimental result show that model-based particle filter have better head pose estimation than classic particle filter.

Contents
ABSTRACT(in Chinese)--------------------i
ABSTRACT-------------------------------ii
Contents-------------------------------iv
List of Figures------------------------V
Chapter 1 Introduction-----------------1
Chapter 2 Related Works----------------8
2.1 Particle filter--------------------8
2.2 Active appearance model------------22
2.3 POSIT------------------------------26
Chapter 3 Proposed Method--------------29
Chapter 4 Regression Analysis----------38
Chapter 5 Experiment-------------------45
5.1 Experiment Database and datasets---45
5.1.1 Bosphorus Database---------------45
5.1.2 BU face tracking datasets--------45
5.1.3 HPEG Datasets--------------------45
5.2 Experiment Result------------------49
Chapter 6 Conclusion-------------------69
References-----------------------------71

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