在科技日新月異的時代,資訊化的地球村是一個未來必然的趨勢.因而,人類的各式各樣的生活也慢慢的被多媒體所取代.並且有不少虛擬化的技術就逐漸問世了.再由於網路技術日漸的發達,透過多媒體的技術在網路上進行各種活動不再是夢想.一個叫做虛擬會議的技術就待開發出來.世界各地的不同地方的人與虛擬人可以在自己的電腦前透過網路的傳輸,在虛擬的世界中一同進行會議.在人們日漸繁忙的生活中不失為一個極大的福音.
然而,虛擬會議是建構在一個三維的影音環境中.每個使用者的頭部動作會對應到一個三維建構的人頭.如何把人臉在影片上的動作以及表情轉化成一連串的動作參數.進而控制三維人頭動作已是一個重要的課題.在編碼標準MPEG4中,定義了一個叫做臉部動態參數FAPs (Facial Animation Parameters)的參數.各個表情以及臉部動作都可以由這個參數來表示.因此將此參數應用在虛擬會議上,可有效率的進行資料上的壓縮及傳輸.

在把臉部表情轉換成運動參數前,必先把臉部的各個特徵點的位置找尋出來,進而進行追蹤.才能即時的分析臉部的動作參數.因此追蹤臉部的各個特徵點以及估測頭部的轉動動向是一個重要前置處理.在本篇論文中主要的就是要探討這兩個問題.然而,臉部特徵點的位置和頭部的動向會有一個關係存在.因此在本篇論文中就要把這兩個問題看成一個問題一併解決.其中還利用了卡爾曼濾波器來輔助.達到不錯的效果,在後文中會一一討論.

In the global village of information, some virtualized technologies are now in bud. A technology called virtual meeting is still being developed. People from every part of the world can participate in the meeting through network communication on his or her computer at office. It is a good news to our busy life.
However, virtual meeting needs a 3D virtual environment and adopts 3D facial models for character representation. The head motions of each user will be mapped to a 3D constructed head model. Therefore, it is an important topic to control the 3D head model by extracting motion parameters from human facial action units in the video sequence. Owing to the popularization of multimedia applications, the ISO MPEG-4 Committee is carrying on the standardization for multimedia communications .One essential issue of the standard is the synthetic and natural hybrid coding(SNHC),which includes 2D images and 3D objects like human bodies. According to the SNHC, a face object is defined with two kinds of parameters: the Facial Definition Parameters (FDPs) and the Facial Animation Parameters (FAPs). The FDPs are used to customize the facial model to a particular face while the FAPs are used for representation of most natural facial expressions. Applying these parameters on a virtual meeting can effectively achieve high data compression and enable augmented reality.

Before abstracting face expressions to FAP parameters, it is necessary to find out the positions of each facial feature point. Hence, to track each feature point, and estimate head orientation is important pre-processing. In this thesis we mainly discuss about these two problems. It exists a relation, however, between position of face feature points and head orientation. We combine these problems in this thesis and try to find a solution for it. In addition, the aid of Kalman filter offers large improvements of the ability in tracking and estimation.

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