3D face reconstruction using images from cameras with varying parameters

被引：18
作者：
Merras M. [1 ]
El Hazzat S. [1 ]
Saaidi A. [1 ,2 ]
Satori K. [1 ]
Nazih A.G. [1 ]
机构：
[1] LIIAN, Department of Mathematics and Computer Science, Faculty of Sciences, Dhar-Mahraz, Sidi Mohamed Ben Abdellah University, B. P 1796, Atlas-Fez
[2] LSI, Department of Mathematics, Physics and Computer Science, Polydisciplinary Faculty of Taza, Sidi Mohamed Ben Abdellah University, B. P 1223, Taza
来源：
International Journal of Automation and Computing | 2017年 / 14卷 / 06期
关键词：
3D face; 3D mesh; 3D reconstruction; Camera calibration; genetic algorithm;
D O I：
10.1007/s11633-016-0999-x
中图分类号：
学科分类号：
摘要：
In this paper, we present a new technique of 3D face reconstruction from a sequence of images taken with cameras having varying parameters without the need to grid. This method is based on the estimation of the projection matrices of the cameras from a symmetry property which characterizes the face, these projections matrices are used with points matching in each pair of images to determine the 3D points cloud, subsequently, 3D mesh of the face is constructed with 3D Crust algorithm. Lastly, the 2D image is projected on the 3D model to generate the texture mapping. The strong point of the proposed approach is to minimize the constraints of the calibration system: we calibrated the cameras from a symmetry property which characterizes the face, this property gives us the opportunity to know some points of 3D face in a specific well-chosen global reference, to formulate a system of linear and nonlinear equations according to these 3D points, their projection in the image plan and the elements of the projections matrix. Then to solve these equations, we use a genetic algorithm which consists of finding the global optimum without the need of the initial estimation and allows to avoid the local minima of the formulated cost function. Our study is conducted on real data to demonstrate the validity and the performance of the proposed approach in terms of robustness, simplicity, stability and convergence. © 2016, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany.
引用
收藏
页码：661 / 671
页数：10
相关论文
共 28 条
[11]  
Scharstein D., Szeliski R., A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, International Journal of Computer Vision, 47, 1-3, pp. 7-42, (2002)
[12]  
Barbalios N., Nikolaidis N., Pitas I., 3D human face modeling from uncalibrated images using spline based deformation, Proceedings of Computer Vision Theory and Applications-VISAPP, pp. 455-459, (2008)
[13]  
Hassanpour R., Atalay V., Delaunay triangulation based 3D human face modeling from uncalibrated images, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshop, IEEE, Washington DC, USA, (2004)
[14]  
Ho S.Y., Huang H.L., Facial modeling from an uncalibrated face image using a coarse-to-fine genetic algorithm, Pattern Recognition, 34, pp. 1015-1031, (2001)
[15]  
Amenta N., The crust algorithm for 3D surface reconstruction, Proceedings of the 15th Annual Symposium on Computational Geometry, pp. 423-424, (1999)
[16]  
A-Nasser A., Mahoor M.H., Abdel-Mottaleb M., 3D face mesh modeling for 3D face recognition, State of The art in Face Recognition, (2009)
[17]  
Saaidi A., Tairi H., Satori K., Fast stereo matching using rectification and correlation techniques, Proceedings of the 2nd International Symposium on Communications, Control and Signal Processing, (2006)
[18]  
Holland J.H., Adaptation in Natural and Artificial Systems, (1992)
[19]  
Merras M., Akkad N.E., Saaidi A., Nazih A.G., Satori K., Camera self calibration with varying parameters by an unknown three dimensional scene using the improved genetic algorithm, 3D Research, 6, 1, pp. 1-14, (2015)
[20]  
Harris C., Stephens M., A combined corner and edge detector, Proceedings of the 4th Alvey Vision Conference, pp. 147-151, (1988)
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