1.State Key Laboratory of CADCG,Zhejiang University,Hangzhou 310027 2.School of Science,Zhejiang University of Technology,Hangzhou 310023 3.School of Science,Zhejiang University of Science and Technology,Hangzhou 310023
Abstract An improved 3D face reconstruction method as well as a binocular stereo vision system based on single camera is proposed. Under the assumption that face is symmetrical, the point cloud is optimized automatically by correction and holes filling. Then, a simplified Candide-3 model is used as initial subdivision controlling mesh, locally refined and levelly fitted. Meanwhile, geodesic mapping technique is applied to normalize different expressions and face databases are built respectively. Experimental results show that the proposed stereo vision system improves the reconstruction accuracy and avoids robust decreasing caused by non synchronous shooting of two cameras. Furthermore, subdivision surfaces used as storage saves space and provides theoretical support for comparison. Considering its low cost, the proposed system is feasible to spread in many fields.
[1] Zhao W, Chellappa R, Rosenfeld A, et al. Face Recognition: A Literature Survey. ACM Computing Surveys, 2003, 35(4): 399-458 [2] Rallings C, Thrasher M, Gunter C, et al. The FERET Database and Evaluation Procedure for Face-Recognition Algorithms. Image and Vision Computing, 1998, 16(5): 295-306 [3] Ansart A N, Abdel-Mottaleb M. 3D Face Modeling Using Two Orthogonal Views and a Generic Face Model // Proc of the International Conference on Multimedia and Expo. Baltimore, USA, 2003, Ⅲ: 289-292 [4] Tang L, Huang T. Automatic Construction of 3D Human Face Models Based on 2D Images // Proc of the International Conference on Image Processing. Lausanne, Switzerland, 1996: 467-470 [5] Zhang Yu, Prakash E, Sung E. Hierarchical Modeling of a Personalized Face for Realistic Expression Animation // Proc of the IEEE International Conference on Multimedia and Expo. Lausanne, Switzerland, 2002: 457-460 [6] Zhou Jiali, Zhang Shuyou, Yang Guoping. A 3D Face Reconstruction and Recognition Method Based on Passive Binocular Stereo Vision. Acta Automatica Sinica, 2009, 35(2): 123-131 (in Chinese) (周佳立,张树有,杨国平.基于双目被动立体视觉的三维人脸重构与识别.自动化学报, 2009, 35(2): 123-131) [7] Tsai R Y. A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-The-Shelf TV Cameras and Lens. IEEE Journal of Robotics Automation, 1987, 3(4): 323-344 [8] Naohide U, Takuma S, Takafumi A, et al. 3D Face Recognition Using Passive Stereo Vision [EB/OL]. [2005-09-11]. http://www.aoki.ecei.tohoku.ac.jp/research/docs/cr2493.pdf [9] Tsai R Y. An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision // Proc of the Conference on Computer Vision and Pattern Recognition. Austin, USA, 1986: 364-374 [10] Weng J, Cohen P, Herniou M. Calibration of Stereo Cameras Using a Non-Linear Distortion Model // Proc of the International Conference on Pattern Recognition. Atlantic City, USA, 1990: 246-253 [11] Bacakoglu H, Kamel M S. A Three-Step Camera Calibration Method. IEEE Trans on Instrumentation and Measurement, 1997, 46(5): 1165-1172 [12] Heikkila J, Silven O. A Four-Step Camera Calibration Procedure with Implicit Image Correction // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Puerto Rico, USA, 1997: 1106-1l12 [13] Weng Juyang, Cohen P, Herniou M. Camera Calibration with Distortion Models and Accuracy Evaluation. IEEE Trans on Pattern Analysis and Machine Intelligence, 1992, 14(10): 965-980 [14] Zhang Zhengyou. A Flexible New Technique for Camera Calibration. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334 [15] Zhang Zhengyou. Flexible Camera Calibration by Viewing a Plane from Unknown Orientations // Proc of the IEEE International Conference on Computer Vision. Kerkyra, Greece, 1999: 666-673 [16] Lü Chaohui, Zhang Zhaoyang, An Ping. Camera Calibration for Stereo Vision Based on Neural Network. Chinese Journal of Mechanical Engineering, 2003, 39(9): 93-96 (in Chinese) (吕朝辉,张兆杨,安 平.基于神经网络的立体视觉摄像机标定.机械工程学报, 2003, 39(9): 93-96) [17] Zhao Qingjie, Sun Zengqi, Lan Li. Neural Network Technique in Camera Calibration. Control and Decision, 2002, 17(3): 336-338 (in Chinese) (赵清杰,孙增圻,兰 丽.摄像机神经网络标定技术.控制与决策, 2002, 17(3): 336-338) [18] Jun J, Kim C. Robust Camera Calibration Using Neural Network // Proc of the IEEE Region 10 Conference. Cheju Island, Korea, 1999: 694-697 [19] Liu Hongjian, Luo Yi, Liu Yuncai. Variable Precision Camera Calibration Using Neural Network. Optics and Precision Engineering, 2004, 12(4): 443-448 (in Chinese) (刘宏建,罗 毅,刘允才.可变精度的神经网络摄像机标定法.光学精密工程, 2004, 12(4): 443-448) [20] Ahmed M T, Hemaved E, Farag A. A Neural Approach for Single-and Multi-Image Camera Calibration // Proc of the IEEE International Conference on Image Processing. Kobe, Japan, 1999, Ⅲ: 925-929 [21] Zhang Ke, Xu Bin, Tang Lixin, et al. Camera Calibration of Binocular Vision System Based on BP Neural Network. Machinery Electronics, 2005, 12: 12-14 (in Chinese) (张 可,许 斌,唐立新,等.基于BP神经网络的双目视觉系统摄像机标定.机械与电子, 2005, 12: 12-14) [22] Zhou Jiali, Zhang Shuyou. A Modeling and Comparison Method for Foot Based on Passive Stereo Vision. Journal of Computer-Aided Design Computer Graphics, 2009, 21(6): 782-788 (in Chinese) (周佳立,张树有.基于被动立体视觉的脚型建模与比对方法.计算机辅助设计与图形学学报, 2009, 21(6): 782-788) [23] Yan Li, Duan Fajie. Optimum Design and Accuracy Analysis of Monocular Stereoscopic Vision Sensor System. Chinese Journal of Sensors and Actuators, 2006, 19(2): 349-352 (in Chinese) (闫 丽,段发阶.单目立体视觉传感器的优化设计及精度分析.传感技术学报, 2006, 19(2): 349-352) [24] Barron A R. Universal Approximation Bounds for Superposition of a Sigmoidal Function. IEEE Trans on Information Theory, 1993, 39(3): 930-945 [25] Everingham M, Zisserman A. Regression and Classification Approaches to Eye Localization in Face Images // Proc of the 7th International Conference on Automatic Face and Gesture Recognition. Southampton, UK, 2006: 441-446 [26] Lienhart R, Maydt J. An Extended Set of Haar-Like Features for Rapid Object Detection // Proc of the IEEE International Conference on Image Processing. New York, USA, 2002, Ⅰ: 900-903 [27] Lienhart R, Kuranov A, Pisarevsky A. Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection // Proc of the 25th Symposium on Pattern Recognition. Magdeburg, Germany, 2003: 297-304 [28] Ahlberg J. CANDIDE-3-An Updated Parameterized Face. Technical Report, LiTH-ISY-R-2326, Linkping, Sweden: Linkping University. Department of Electrical Engineering, 2001 [29] Suzuki H, Takeuchi S, Kanai T. Subdivision Surface Fitting to a Range of Points // Proc of the 7th Pacific Conference on Computer Graphics and Applications. Seoul, Korea, 1999: 158-167 [30] Bronstein A M, Bronstein M M, Kimmel R. Three-Dimensional Face Recognition. International Journal of Computer Vision, 2005, 64(1): 5-30 [31] Bronstein A M, Bronstein M M, Kimmel R. Expression-Invariant Representation of Faces. IEEE Trans on Image Processing, 2007, 16(1): 188-197 [32] Wang Yueming. Research on 3D Face Recognition across Expression. Ph.D Dissertation. Hangzhou, China: Zhejiang University. College of Computer Science and Technology, 2007 (in Chinese) (王跃明.表情不变的三维人脸识别研究.博士学位论文,杭州:浙江大学.计算机科学与技术学院, 2007)
2010, Vol. 23 
