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研究生:何柏翰
研究生(外文):Po-HangHo
論文名稱:基於人臉紋理變化之情緒辨識系統
論文名稱(外文):An Emotion Recognition System Based on Facial Texture Variation
指導教授:楊家輝楊家輝引用關係
指導教授(外文):Jar-Ferr Yang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
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:38
中文關鍵詞:局部二元化圖樣主動形狀模型支持向量機情緒辨識
外文關鍵詞:LBPASMSVMEmotion recognition
相關次數:
  • 被引用被引用:0
  • 點閱點閱:264
  • 評分評分:
  • 下載下載:17
  • 收藏至我的研究室書目清單書目收藏:0
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。
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

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


[1]M. Suwa, N. Sugie, and K. Fujimora, “A preliminary note on pattern recognition of human emotional expression, in Proc. Int. Joint Conference on Pattern Recognition, Kyoto, Japan, pp. 408-410,1978
[2]P. Ekman and W. V. Friesen, “Constants across cultures in the face and emotion, Journal of Personality and Social Psychology, vol. 17, pp. 124-129, 1971.
[3]Viola, P., Jones, M.: “Rapid object detection using a boosted cascade of simple features. In: Proc. Conf. Computer Vision and Pattern Recognition(CVPR). Volume 1.,Kauai, HI, 511–518 USA (2001)
[4]W. S. Yambor, B. A. Draper, and J. R. Beveridge, “Analyzing PCA-based face recognition algorithm: eigenvector selection and distance measures,July 2000
[5]A.M. Martinez, A.C. Kak, “PCA versus LDA, IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 23, No. 2, pp. 228-233, 2001
[6]C. Shan, S. Gong and P. W. McOwan, “Robust facial expression recognition using local binary patterns ,IEEE Conference on Image Processing,2005
[7]T. Ahonen,, A. Hadid, M.P. inen “Face description with local binary patterns: application to face recognition IEEE Trans. On Pattern Analysis and Machine Intelligence, 2006
[8]Christopher, and J. C. Burges, “A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery,Vol.2 , Issue 2, pp.121 - 167, 1998
[9]G. Guo, S.Z. Li, K. Chan, “ Face recognition by support vector machines, Proc. Of the IEEE Conference on Automatic Face and Gesture Recognition, Grenoble, France, pp. 196-201, 26-30 March 2000.
[10]R. Lienhart and J. Maydt, “An extended set of haar-like features for rapid object detection, Submitted to ICIP2002.
[11]T. Cootes, C. Taylor, D. Cooper and J. Graham,“Active shape models –their training and their applications, Computer Vision and Image Understanding, 61(1), pp. 38-59, January 1995.
[12]A. Lanitis, C. J. Taylor, T. F. Cootes, “Interpretation and coding of face images using flexible models, IEEE Trans. on Automatic Pattern Analysis & Machine Intelligence, vol.19, no.7, pp.743-56, July 1997.
[13]T. Ojala, M. Pietik¨ainen, and D. Harwood. “A comparative study of texture measures with classification based on feature distributions.Pattern Recognition, 29:51–59, 1996.
[14]J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, “Face recognition using LDA-based algorithms, IEEE Trans. on Neural Networks, Vol. 14, No. 1, pp. 195-200, January 2003
[15]Hsu C. W., and C. J. Lin, A comparison of methods for multi-class support vector machines, IEEE Trans. on Neural Networks, pp.415-425, 2002.
[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]
[17]The Japanese Female Facial Expression (JAFFE) Database [Online]. Available: http://www.kasrl.org/jaffe.html

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