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系統識別號 U0026-0908201215491900
論文名稱(中文) 基於人臉紋理變化之情緒辨識系統
論文名稱(英文) An Emotion Recognition System Based on Facial Texture Variation
校院名稱 成功大學
系所名稱(中) 電腦與通信工程研究所
系所名稱(英) Institute of Computer & Communication
學年度 100
學期 2
出版年 101
研究生(中文) 何柏翰
研究生(英文) Po-Hang Ho
學號 Q36991403
學位類別 碩士
語文別 英文
論文頁數 38頁
口試委員 指導教授-楊家輝
召集委員-邱瀞德
口試委員-張添烜
口試委員-吳宗憲
口試委員-連震杰
中文關鍵字 局部二元化圖樣  主動形狀模型  支持向量機  情緒辨識 
英文關鍵字 LBP  ASM  SVM  Emotion recognition 
學科別分類
中文摘要 自動化人臉情緒辨識系統一直是電腦視覺領域上的熱門研究,而這項技術也使的電腦更加的人性化。自動化人臉情緒辨識系統與許多領域息息相關,例如智慧生活以及醫學領域方面。在這篇論文中,我們使用人臉辨識常用的局部二元化圖樣來進行情緒辨識,並提出新的方法來改善結果。傳統的情緒辨識,會先利用Viola-Jones的方法,從影像中擷取出人臉。然而情緒辨識中,人的五官是比較重要的資訊,透過Viola-Jones擷取的人臉會有許多不必要的資訊如頭髮、耳朵以及背景。本文提出以主動形狀模型的方式來擷取人臉並保留重要的資訊。最後搭配支持向量機來分類開心、難過、厭惡、害怕、驚訝以及生氣這六種表情。
英文摘要 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.
論文目次 Table of contents

摘要 I
Abstract II
誌謝 III
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
References 37

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[16] Cohn-Kanade AU-Coded facial expression database. [Online]. Available: http://vasc.ri.cmu.edu/idb/html/face/facial_expression/index.html July 2008 [date accessed]
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