微表情是一種持續時間極為短暫的表情，它能表現出人類試圖壓抑與隱藏的真正情感。本論文以問卷的形式影像訪問受試者，採集臉部變動數據進行分析臉部細微變動的情況、特徵進行量化的研究。藉由臉部嘴巴、眼睛的變動作為指標，建立邏輯斯模型，由參數估計找出每個影響因子對說謊的影響。
本論文對十人進行測謊分析，實證結果發現，嘴巴水平長度越長，越傾向說實話。且嘴巴水平長度與平均長度的差異越大，有較高說謊的機率。眼睛垂直寬度越大，較傾向說實話，且眼睛垂直寬度與平均寬度的差異越大，說謊話的機率越高。當眼睛垂直寬度在極短時間內（一幀）的變動越大，說謊話的可能性越高。
利用五個解釋變數建立了預測模型，本論文的測謊預測模型正確率可達62.17%，實證結果發現受試者在回答涉及隱私及道德性的高風險問題時，能被模型辨識說謊與否的正確率較回答一般資本資料的低風險問題高。若只考慮高風險問題的人臉數據，預測模型能辨識說謊與否的正確率將達到65.38%。

Facial micro-expressions are brief, involuntary expressions appeared on the face of people who are trying to conceal or repress emotions. This paper developed a questionnaire containing control questions and relevant questions. We analyze the motions of key features on the faces and the appearance of the micro-expressions, we quantify the characteristic of telling lies by observing a sequence of frames extracted from videos. In the model, we test several factors which including eyes’ and mouth’s micro movements, and then fit the logistic regression model to find the main effect on the facial movements when telling lies.
The empirical results indicate that people tend to lie while decreasing the horizontal length of their mouths. Furthermore, the vertical width of eyes tends to change significantly in a very short period while lying.
The classification result of model shows that sensitivity = 74.36% as the proportion of deceptive response being classified correctly; and the overall classification accuracy is 62.23%. We also find out that when people are answering high-stakes questions, our model will achieve a 71.79% classification accuracy in deception response with an overall accuracy as 65.38%, which is higher than the accuracy of all data.

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中文參考文獻
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