臺灣博碩士論文加值系統

在視訊監控的應用中，基於臉部特徵的欺騙檢測為具有挑戰性的重要議題。本論文提出了一種基於臉部特徵點之混合分類式欺騙檢測系統，並且應用於分辨識欺騙和事實。
方法上先使用隨機森林分類器來擷取臉部特徵點，在臉部特徵點擷取方面，透過改進隨機森林的分割法則與特徵的擷取，使其能更準確的將樣本分群，並且能抵抗光源、角度等外在因素帶來的影響
在特徵擷取方面，利用臉部特徵點用於分析臉部動作模組、臉部顏色資訊、虹膜移動資訊以上三種特徵彼此間的搭配組合，為了更好地加強檢測欺騙方法，本論文採用最小均方濾波器訓練，以提高提取特徵的強健性。最後透過預先訓練的最小均方濾波器和支持向量機的組合，使其更準確的檢測欺騙以及真相。
在實驗結果方面，本論文使用Real-Life資料庫與自行收集的MSP-YTD資料庫分別進行測試並與前人技術比較，儘管影片中存在不受控制的因素，如照明，頭部姿勢和臉部遮蔽，但從結果可看出所提出的算法皆有良好的準確率，也因此可被應用於現實生活中。

Facial deception detection is becoming a challenging problem for automatic inspection of surveillance videos. In this thesis, we propose a novel algorithm for differentiating the deception and truth based on visual clues. The Random Forest classifier is applied to track the facial landmark points, which is utilized to analyze the facial action unit based on the movement of the facial feature points. In addition, the biological and geometrical features are also considered, and the sequential forward floating selection (SFFS) is integrated to select the best feature combinations. The proposed method employs least-mean-square filter to significantly improve the robustness of the extracted features. To verify the extracted features for deception and truth identification, the pre-trained least-mean-square filters and the Support Vector Machine (SVM) are utilized. Experimental results demonstrated that despite the uncontrolled factors, illumination, head pose and facial of sheltering, in the videos, the proposed method is consistent in achieving promising performance compared to that of the former schemes.

中文摘要I
AbstractII
誌謝III
目錄IV
圖表索引VI
第一章 緒論1
1.1 研究背景與動機1
1.2 論文架構3
第二章 文獻探討4
2.1 特徵擷取相關文獻5
2.1.1 情緒辨識相關研究5
2.1.2 臉部動作編碼系統(Facial Action Coding System)12
2.2 挑選特徵演算法相關文獻16
2.2.1 降低特徵維度(Dimensionality Reduction)16
2.2.2 循序前進演算法(Sequential Forward Selection, SFS)19
2.2.3 循序後退演算法(Sequential Backward Selection, SBS)20
2.2.4 循序前進浮動演算法(Sequential Forward Floating Selection, SFFS)21
2.3 臉部特徵點擷取相關文獻22
2.4 欺騙檢測相關文獻32
第三章 臉部欺騙檢測38
3.1 系統架構39
3.2 前處理41
3.2.1 人臉偵測(Face Detection)41
3.2.2 感興趣之區域擷取(Region Of Interest, ROI)44
3.3 特徵擷取48
3.3.1 生物特徵(Biological Features)48
3.3.2 幾何特徵(Geometric features)49
3.3.3 臉部動作單元(Facial Action Unit)52
3.4 支持向量機(Support Vector Machine)69
3.4.1 線性可分離(Linearly separable)70
3.4.2 線性不可分離(Linearly non-separable)73
3.5 最小均方濾波器(Least-mean-square filter)75
第四章 實驗結果80
4.1 參數最佳化80
4.2 資料庫82
4.3 他人技術比較84
4.4 時間複雜度88
第五章 結論與未來展望90
參考文獻91

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