近年來在人機互動中，臉部表情辨識系統已成為很重要的議題，不論是人與電腦的互動(HCI)或者人與機器人的互動上(HRI)皆是如此。而臉部表情辨識系統中，特徵的擷取是重要的一環。本論文中我們應用區別共同向量來達到表情辨識中特徵的擷取。我們辨識包括六個基本的表情，包括高興、悲傷、生氣、噁心、害怕和驚訝。以區別共同向量的特徵抽取方法，不但可以將高維度的影像資訊降階至低維度空間，並將它們做區別分類，讓辨識階段能更容易成功。最後再以隱藏式馬可夫模型分析其時間軸上的資訊，達到臉部表情辨識的效果。

Recently facial expression recognition system has become an important issue in both human-computer interaction (HCI) and human-robot interaction (HRI). It is an important issue to extract features from face images to recognize facial expression. In this paper, we apply a face feature extraction approach, namely discriminative common vectors, for the recognition of the six basic expressions including happy, sad, angry, disgust, fear and surprise. By applying discriminative common vector, we can reduce the dimensionality of image and classify them in a lower dimension which would be useful in later recognition procedure. Then we use HMM as our classifier to find the time series information of the feature vector projected by common vector.

Abstract (in Chinese) i
Abstract ii
Acknowledgement (in Chinese) iii
Contents iv
List of tables v
List of figures vi
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 1
1.3 Review of Literature 2
Chapter 2 Spatial-Temporal Common Vector 7
2.1 Discriminative Common Vector (DCV) 7
2.2 Temporal DCV 11
Chapter 3 The Expression Recognition System 15
3.1 System Overview 15
3.2 Preprocessing 18
3.2.1 Face Region 18
3.2.2 Optical Flow 20
3.3 Recognition 21
3.3.1 Continuous Hidden Markov Model 23
Chapter 4 Experimental Results 29
4.1 Database 29
4.2 State Number Comparison 30
4.3 Compared with voting 31
Chapter 5 Conclusions 34
References 35

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