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研究生:徐群芳
研究生(外文):Cyun-Fang Hsu
論文名稱:使用連續型馬可夫模型從事臉部表情辨識
論文名稱(外文):Facial Expression Recognition Using Continuous Markov Model
指導教授:王元凱王元凱引用關係
指導教授(外文):Yuan-Kai Wang
學位類別:碩士
校院名稱:輔仁大學
系所名稱:電子工程學系
學門:工程學門
學類:電資工程學類
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:77
中文關鍵詞:臉部表情辨識馬可夫模型高斯混合模型臉部幾何框架
外文關鍵詞:Facial Expression RecognitionMarkov ModelGaussian Mixture ModelGeometrical Facial Framework
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本文提出一個快速的自動化臉部表情辨識系統,此系統藉由簡化特徵萃取與辨識器架構達到加快執行速度的效果。在特徵萃取上,首先使用混入查表法概念的機率式膚色模型快速地取得臉部區域,接著利用階層式臉部幾何框架有效地估計臉部特徵的範圍,輔以適應性梯度遮罩對抗光源變化和適應各種大小的臉和臉部特徵,以得到穩定的邊緣資訊,用於量測臉部特徵的形變。在辨識上,由於臉部表情動作時,臉部特徵呈現時序性變化,因此使用架構簡單且能分析時序性資料的連續型馬可夫模型(Continuous Markov Model),不僅可強化臉部表情辨識的正確性,更能有效地加快辨識速度。實驗結果證實所提出的系統能夠有效地解決由複雜場景、光源變化和攝影距離所造成的問題,且相較於連續型隱藏式馬可夫模型(Continuous Hidden Markov Model)此系統擁有較快的辨識速度與相近的辨識率。
We propose a fast and automated facial expression recognition system by simplifying both feature extraction and classification. In feature extraction, a skin probability model which adopts look-up table method is devised to swiftly detect the face in the image. Then a multi-layer geometric framework is applied to effectively find the region of facial features. We also use a novel adaptive gradient mask to obtain steady edge information against illumination change and facial feature deformation. In recognition, the deformations of facial features are modeled as stochastic processes by a continuous Markov model which has a simple structure to statistically model time-varying data of facial expression with fast recognition speed and without loss of accuracy. Experimental results show the proposed system can effectively recognize facial expressions from cluttered scenes, illumination change and depth. Comparing to continuous hidden Markov model, the proposed system has great improvement in performance.
摘要 i
英文摘要 ii
誌 謝 iii
目 錄 iv
表目錄 vi
圖目錄 vii
第一章 導論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 相關研究 2
第二章 系統架構 11
2.1 樣本挑選 12
2.2 臉部定位 12
2.3 特徵萃取 13
2.4 辨識器選取 15
第三章 臉部定位 16
3.1 色彩空間 17
3.2 膚色模型 17
3.3 擷取臉部區域 22
第四章 特徵萃取 26
4.1 邊緣強化 27
4.2 群聚式數值分析法 30
4.3 階層式臉部幾何框架 32
4.3.1 校正臉部上邊界 33
4.3.2 校正臉部左右邊界 36
4.3.3 校正臉部下邊界 39
4.3.4 眼睛垂直軸位置定位 42
4.3.5 眼尾水平軸位置定位 44
4.3.6 眉頭位置定位 45
4.3.7 嘴唇定位 47
4.3.8 嘴角定位 49
4.4 特徵篩選 51
第五章 辨識 53
5.1 連續型隱藏式馬可夫模型 55
5.2 連續型馬可夫模型 61
第六章 實驗結果 66
第七章 結論 74
參考文獻 75
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