||An Emotion Recognition System Based on Facial Texture Variation
||Institute of Computer & Communication
The automatic emotion recognition system has been a popular issue in computer vision area. With emotion recognition system, computer becomes more humanized. It also brings strong impacts on many areas such as smart living and medical area. In this thesis, I use the LBP method, which was commonly used in facial expression recognition. Furthermore, We propose a novel idea to improve the result. In traditional facial expression recognition, the researchers use Viola-Jones method to crop face from input image. However, the cropped face contains unimportant information such as hair, ear and background. Thus, ASM method was used to adjust the cropped face and keep important information. Finally, we distinguish six expressions as happiness, sadness, disgust, fear and surprise with SVM.
Table of contents
List of tables VI
List of figures VII
1 Introduction 1
1.1 Research background 1
1.2 Motivation 2
1.3 Related work 2
1.4 The structure of facial recognition 4
1.5 Summary of the thesis 5
2 Related research 6
2.1 Face detection 6
2.1.1 Integral image 6
2.1.2 Harr feature and adaboost algorithm 8
2.1.3 Cascade classifiers 9
2.1.4 Active shape model 9
2.2 Feature extraction 11
2.2.1 Principal component analysis 11
2.2.2 Local binary pattern 14
2.3 Support vector machine 16
2.3.1 Linearly separable 19
2.3.2 Linearly non-separable 20
2.3.3 Non-linearly separable 21
3 Proposed system 24
3.1 Facial expression recognition system 24
3.2 Face detection and pre-processing 25
3.2.1 Calibration with ASM 25
3.2.2 Normalization 26
3.3 Texture extraction 27
3.4 Classification with support vector machine 28
3.4.1 One-against-rest method 29
3.4.2 One-against-one method 30
4 Experimental results 32
4.1 System environment 32
4.2 Experimental results 34
5 Conclusions 36
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