隨著電腦多媒體影像及網路技術的蓬勃發展，建立細緻的仿真人頭顱模型遂成為相當重要的研究課題。由於顱顏的特徵多且細緻，為了提供即時動態表情變化的需求，因此需要一個兼具延展性網格又保有幾何特徵的顱顏模型。特徵辨識技術上，常見研究以手動或半自動方式設定特徵，然而以人工或半手工方式進行辨識常缺乏客觀性、唯一性與重現性。因此，本研究提出一套客觀且自動化的特徵辨識方法，利用搜尋得的幾何特徵點、特徵線重建頭顱模型，且可依應用需求決定網格之精細程度，在不失去特徵的情況下，簡化資料量。
　　本文改善人體頭部特徵萃取與重建研究的方法，以MPEG-4定義之特徵幾何意義，建立有系統且客觀的自動化萃取顱顏特徵的方法。本研究並針對歪斜的頭顱提出校正的方法，以提高特徵的辨識率，並整合電腦斷層掃描影像所重建的耳朵解決兩耳特徵辨識的困難。依應用領域需求決定網格的細緻程度，以建立具備特徵的可擴展性顱顏模型，可應用於多媒體三維影像傳輸、電腦動畫模擬等領域。

Human head reconstruction becomes an important research topic while the computer graphic technologies developing in past few decades. Because of large amount of the face features are complex, and the needs of the real-time animation of the facial expressions, it is necessary to elaborate the head model. In the past, researchers often selected the features by hands. It is a subjective method. This research uses an objective and automatic method to locate the features on the head. The reconstruction of head model is according to the feature points and lines, and provides different levels of details to fit different requirements. All of these levels of meshes will not lose the features.
This article improves the method described in “Feature-based Digital Head Reconstruction.” Systematically and objectively extract features automatically according to the MPEG-4 definition. This research also introduces a method that can rectify the tilt head to enhance the recognition, and a method that can replace the poorly sampled ear data from the body scanner by a better one from the CT image. The extendable feature-based head model can be easily changed the density of the meshes according to the requirement. It is much better suitable for the applications of data transmission across the internet and computer graphics animation.

[1] Abrantes, G, and Pereira, F. “MPEG-4 Facial Animation Technology: Survey, Implementation, and Results.” IEEE Trans on Circuits and Systems for Video Technology, Vol. 9, No. 2, pp.290-305, 1999.
[2] Anad, V. B., 1993. Computer Graphics and Geometric Modeling for Engineeris., John Wiley & Sons, Inc.
[3] Babu, S., Suma, E., Barnes, T., and Hodges, L. F., “Can Immersive Virtual Human Teach Social Conversational Protocols?” IEEE Virtual Reality Conference, pp.215-218, 2007.
[4] Breton, G., Bouville, C., and Pele, D., “FaceEngine A 3D Facial Animation Engine for Real Time Applications,” Proceedings of the Sixth International Conference on 3D Web Technology, pp.15-22, 2001.
[5] Cosatto, E., and Graf, H. P., “Sample-based Synthesis of Photo-Realistic Talking Heads,” Computer Animation 98 Proceeding, pp.103-110, 1998.
[6] Garchery, S., Egges, A., and Magnenat-Thalmann, N., “Fast Facial Animation Design for Emotional Virtual Humans,” Proceedings of Measuring Behaviour, 2005.
[7] Gordon, G. G., “Face Recognition based on Depth Maps and Surface Curvature,” Proceedings of Geometric Methods in Computer Vision, pp. 234-247, 1991.
[8] Goto, T., Escher, M., Zanardi, C., and Magnenat-Thalmann, N., “MPEG-4 based Animation with Face Feature Tracking,” Proceedings of Eurographics Workshop on Computer Animation and Simulation ’99, pp.89-98, 1999.
[9] Gu, H., Su, G., and Du, C., “Feature Points Extraction from Faces,” Proceedings of Image and Vision Computing New Zealand Conference, pp.154-158, 2003.
[10] Hibbeler, R. C., 2000. Mechanics of Materials., Prentice Hall, Inc.
[11] Jeng, S. H., Liao, H. Y. M., Y. T. Liu, and Chern, M. Y., “An Efficient Approach for Facial Feature Detection Using Geometrical Face Model,” Proceedings of the International Conference of Pattern Recognition, pp. 1739-55, 1996.
[12] Jesorsky, O., Kirchberg, K. J., and Frischholz, R. W., “Robust Face Detection Using the Hausdorff Distance,” Proceedings of the Third International Conference on Audio- and Video-based Biometric Person Authentication, Springer, Lecture Notes in Computer Science, pp.90–95, 2001.
[13] Kanhler, K., Haber, J., and Seidel, H. P., “Geometry-based Muscle Modeling for Facial Animation,” Proceedings of Graphics Interface, pp.37-46, 2001.
[14] Lam, K. M., and Yan, H., “Locating and Extracting the Eye in Human Face Image,” the 13th International Conference on Pattern Recognition, pp.771-779, 1996.
[15] Lam, K. M., and Li, Y. L., “An Efficient Approach for Facial Feature Detection,” the Fourth International Conference on Signal Processing, Vol. 2, pp.1100-1103, 1998.
[16] Lee, Y., Terzopoulos, D., and Waters, K., “Realistic Modeling for Facial Animation,” Proceedings of SIGGRAPH 95, pp.55-61, 1995.
[17] Lee, R. S. T., and Liu, J. N. K., “An Integrated Elastic Contour Fitting and Attribute Graph Matching Model for Automatic Face Coding and Recognition”, Third International Conference on Knowledge-Based Intelligent Information Engineering Systems, pp.292-295, 1999.
[18] Lee, W.-S. and Magnenat-Thalmann, N., “Fast Head Modeling for Animation,” Image and Vision Computing, Vol.18, No.4, pp.355-364, 2000.
[19] Rob, K., “Overview of the MPEG-4 Standard,” ISO/IEC JTC1/SC29/WG11 N4030, March 2001.
[20] Tsai, M. J., and Fang, J. J., “A Feature Based Data Structure for Computer Manikin,” U.S. Patent (Pending), 2004.
[21] Parke, F. I., “Parameterized Models for Facial Animation,” IEEE Computer Graphics and Applications, Vol.2, No.9, pp.61-68, 1982.
[22] Piegl, L., and Tiller, W., 1997. the NURBS Book 2nd Edition, Srpinger-Verlag, Inc.
[23] Wang, M. J. J., Wu, W. Y., Huang, L. K., and Wang, D. M., “Corner Detection Using Bending Value”, Pattern Recognition Letters 16, pp.575-583, 1995.
[24] Waters, K., and Terzopoulos, D., “A Physical Model of Facial Tissue and Muscle Articulation,” Proceedings of the First Conference on Visualization in Biomedical Computing, pp.77-82, 1990.
[25] Xue, Z., Li, S. Z., and Teoh, E. K., “Facial Feature Extraction and Image Warping Using PCA Based Statistic Model,” International Conference on Image Processing, pp.689-692, 2001.
[26] Yan, P., and Bowyer, K. W., “Ear Biometric Using 2D and 3D Images,” Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.121-128, 2005.
[27] Yen, G. G., and Nithianandan, N., “Facial Feature Extraction Using Genetic Algorithm,” Proceedings of the Evolutionary Computation on 2002, pp.1895-1990, 2002.
[28] Zhang, Y., and Sim, T., “Generating Animatable 3D Virtual Faces From Scan Data,” Proceedings of the 2003 IEEE International Workshop on Robot and Human Interactive Communication Millbrae, pp.43-48, 2003.
[29] Zhang, L., Snavely, N., Curless, B., and Seitz, S. M., “Spacetime Faces: High Resolution Capture for Modeling and Animation,” ACM Transactions on Graphics (TOG), Vol. 23, No. 3, pp.548-558, 2004.
[30] 王奎武、王洵、董蘭芳、陳意雲，一個MPEG-4兼容的人臉動畫系統，計算機研究與發展，第三十八卷，第五期，529-535頁，2001。
[31] 呂芳懌、余少棠、許閔雄、林晁立，影像資料庫在人臉辨識上的應用-前科犯資料庫，東海科學第一卷，103-122頁，1999年7月。
[32] 張英良，以實際影像序列為依據之人臉動作模擬，國立中山大學資訊工程研究所碩士論文，2002。
[33] 黃盈倫，人體頭部特徵萃取與重建，國立成功大學機械工程研究所碩士論文，2004。
[34] 楊孟哲，Delaunay三角化於臉部辨識之應用，國立中山大學應用數學研究所碩士論文，1999。
[35] 蔡明俊、黃宏龍、陳治評、林長慶，三度空間人體掃描機，中華民國專利，新型第206392號，2003。