現今的研究中3D人臉模型的重建仍是一個重要主題。由於2D的人臉辨識及合成技術已遭遇到瓶頸，3D人臉模型在辨識與合成上可以提供優於2D人臉影像的準確率與效能。但3D掃描設備價格高昂不易取得，從2D影像序列中重建出3D模型是一個較方便的方法。
從2D影像中重建3D人臉模型有兩個主要的研究方向，第一個是可形變模型(Morphable Model)，其準確度較高。另一個方法則是從運動資訊來估測3D模型(SfM)，此方法的重建速度較快。
在這篇論文中，我們提出一個從影像序列自動重建三維人臉模型的改良系統，此系統包含了一個前處理、AAM特徵點萃取，3D模型重建以及模型的精煉。重建人臉模型的部分是基於從運動資訊估測3D模型的方法。我們在這個系統上加上了一個姿態分類的前處理來尋找影像序列中的關鍵影像，以減少對整個影像序列萃取特徵點的時間。最後提出了一個模型精煉的方法來增加模型的精細度。
我們的方法可以有效減少特徵點萃取所需的時間，並得到一個品質不錯的3D人臉模型。系統整體運算時間也在可接受的合理範圍內。

3D Face Reconstruction is an important research topic nowadays. Because of the bottleneck on 2D Face Recognition and Face Synthesis technique, 3D Face Model has better accuracy and performance than 2D Face Image on Recognition and Synthesis. But 3D scan equipment is really expensive and hard to acquire. 3D model Reconstruction from 2D images is a cheaper way to get 3D data.
There are 2 main approaches for 3D face model reconstruction from 2D images. The first approach is Morphable Model proposed by Volker Blantz. Another approach is a factorization method based on Structure from Motion (SfM) proposed by Kanade. Morphable model has high accuracy, but takes much time for model fitting. SfM has lower accuracy than morphable model, but SfM is fast.
In this thesis, an automatic 3D model reconstruction system in image sequence is proposed. This system is based on SfM method. It contains modifying AAM feature extraction, model reconstruction and a refinement process we proposed.
In the experiment, our method can reduce the time spent in the feature extraction process. Our method also can reconstruct a 3D model which has good quality. The speed of this system is also acceptable.

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