臺灣博碩士論文加值系統

本論文中，提出一個基於特徵點追蹤之人臉追蹤技術。本論文提出之人臉追蹤技術主要由兩個部份所組成：人臉偵測和人臉追蹤。
首先，人臉偵測部分，使用Haar-Like特徵結合多分類器演算法的人臉偵測器，找出人臉的所在區域，為了移除偵測錯誤的人臉區域，人眼資訊也被採用。完成人臉偵測後，本論文透過特徵擷取與特徵追蹤達成人臉追蹤。
為了評估所提出的方法，藉由網路攝影機擷取各種不同的臉部動態影像。實驗結果顯示，本論文提出之人臉追蹤系統中，人臉偵測正確檢測率高於96%，人臉追蹤正確檢測率高於91%，效果良好。實驗結果證明，在自然且具有雜訊環境下，本論文提出的方法可以有效地達成人臉追蹤。

In the thesis, we implemented a face detection and tracking system. The developed system is composed of two main parts: face detection and face tracking.
In the face detection part, a face detector using Haar-Like features trained by Adaboost algorithm is adopted to detect facial region. To remove the error of face region, the human eyes information can also be used. After the face detection was completed, each face candidate can be tracked in the temporal domain. In the face tracking part, KLT features are extracted and tracked between two adjacent frames. Based on KLT feature tracking, face tracking can be achieved in the developed system.
To evaluate the developed system, several videos with different kinds of face movement are captured by using low-cost webcam. Experimental results show that our proposed system can detect and track facial regions well. The detection rate of our face detection is more than 96% and the detection rate of our face tracking is more than 91%. These results demonstrate that our proposed system can achieve face detection and face tracking in real-world noisy videos.

封面內頁
簽名頁
中文摘要 iii
ABSTRACT iv
誌謝 v
目錄 vi
圖目錄 viii
表目錄 x

第一章 緒論 1
1.1 研究動機 1
1.2 系統概要 1
1.3 人臉偵測相關技術 3
1.3.1 人臉追蹤困難之處 3
1.4 人臉追蹤相關技術 3
1.4.1 人臉追蹤困難之處 4
第二章 人臉偵測 5
2.1 人臉偵測之系統架構 5
2.2 人臉矯正 6
2.2.1 結構相似性指標 8
2.2.2 旋轉人臉影像 9
2.2.3 亮度調整 12
2.3 人眼偵測 14
2.3.1 局部二元樣本 16
2.3.2 支持向量機 17
第三章 人臉追蹤 20
3.1 人臉追蹤之系統架構 20
3.2 KLT特徵追蹤 22
3.3 重新人臉偵測之程序 26
第四章 實驗結果 28
4.1 系統執行環境 28
4.2 人臉偵測之結果 29
4.2.1 Haar-Like特徵結合多分類器演算法的人臉偵測之結果 29
4.2.2 人臉旋轉之結果 32
4.2.3 亮度調整與人眼偵測之結果 33
4.2.4 人臉偵測之結果 35
4.2.5 特徵擷取之結果 40
4.2.6 人臉追蹤之結果 43
第五章 結論與未來研究方向 46
5.1 結論 46
5.2 未來研究方向 46
參考文獻 47

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