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.

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