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Author: 徐金豪
Chin-Hao Hsu
Thesis Title: 基於修改主動外觀模型特徵之臉部表情辨識研究
Study of Modified Active Appearance Models Features for Facial Expression Recognition
Advisor: 吳怡樂
Yi-Leh Wu
Committee: 唐政元
Cheng-Yuan Tang
陳延禎
Yen-Jen Chen
鄧惟中
Wei-Chung Teng
Degree: 碩士
Master
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2010
Graduation Academic Year: 98
Language: 英文
Pages: 32
Keywords (in Chinese): 臉部表情辨識主動外觀模型向量支援機平均臉部形狀
Keywords (in other languages): Facial expressions recognition, Active Appearance Models, Support Vector Machine, mean shape
Reference times: Clicks: 365Downloads: 2
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在心理學上被定義的六種表情包括生氣、厭惡、恐懼、快樂、傷心、驚訝等,是最被廣泛用來作為表情辨識研究的依據。在此篇論文我們提出一個有效率的方法去辨識這六種表情,方法主要的內容是去修改利用主動外觀模型所定位出來的原始臉部形狀特徵點。而最後我們利用向量支援機去處理臉部表情分類。
主動外觀模型在各種辨識領域早已被廣泛使用,包含醫學影像辨識,臉部辨識等。它主要藉由蒐集影像的形狀跟紋理建立出一個原始的模型去做影像的辨識與對應,由於它是事先利用手動定義出要辨識的影像型態特徵點,所以一但模型建立後,對於該類別影像的辨識準確率非常高。此篇論文我們將每張臉部影像根據五官定義出68個二維特徵點,並用這些影像訓練出我們所需的主動外觀模型來做臉部形狀的定位。
被利用來做向量支援機分類的原始臉部形狀特徵點是由x軸跟y軸所構成的二維座標。我們藉由去計算原始臉部形狀特徵點與平均臉部形狀特徵點之間的差異所得之差異向量,來作為本篇論文主要分類的依據。
實驗結果證實了我們的方法與原始特徵點的比較,對於每種表情的辨識結果都有顯著的提升,顯示我們的改進方法的確有助於表情辨識率的提升。


The six basic facial expressions (anger, disgust, fears, happy, sadness, surprise) which have been recognized in psychology are commonly used for facial expressions recognition research. In this work, we propose intuitive and effective facial features to recognize the six facial expressions by modifying the original facial feature points used by the original Active Appearance Model (AAM). The Support Vector Machine (SVM) is employed as the base classifier for facial expression classification. The original facial features are two dimensional vertex point position represented by the x- and y- coordinates. However, we observe that every facial expression is just a deviation from the original neutral facial expression, so we try to exploit the differences between any facial expression and the neutral expression for facial expression recognition. The experiment results by applying the proposed differential facial feature, the recognition accuracy is higher than by applying the original facial features for all facial expressions.

1. Introduction…………………………………………………1 2. Related Work…………………………………………………3 3. Feature Extraction…………………………………………5 3.1 The AAM…………………………………………………5 3.1.1 From ASM to AAM……………………………5 3.1.2 AAM Fitting Process………………………6 3.2 Facial Features………………………………………7 4. Support Vector Machine……………………………………11 4.1 The SVM…………………………………………………11 4.2 LIBSVM……………………………………………………13 4.3 SVM Parameter Optimization…………………………13 5. Experiments and Results ……………………………15 5.1 Recognition Results Using the Unbalanced Dataset16 5.2 Recognition Results Using the Balanced Dataset…17 6. Conclusion and Future Work ……………………………21 References………………………………………………………22

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