Abstract
This paper presents a 3D face reconstruction face model by using PCA-based reconstruction model in synthesizing faces of individual. A 3D face reconstruction model is derived by transforming the shape and texture of the training sets into a vector space representation. In this paper, a reconstruction of face model is adapted from 3 Dimensional Face Space (3DFS) with the knowledge of the shape and texture of faces. Faces statistics produced by sampling from Face Space is computed by Principle Component Analysis (PCA) of 100 exemplar 3D faces. 3D face space is formed by two distinctive subspaces: the 3D shape space and 3D texture space which consist of 79-dimensional (79 shape and texture coefficient). The first shape space shows the impact of the shape and texture dimensions and the second texture space shows the influence of the shape and texture dimensions. A vertex is a point where two edges of a 2D polygon or two or more vertices of a 3D polyhedron meet. Face Space Coefficient (FSC) is computed as the input for training in generating novel 3D faces as Wavefront Object files (OBJ). The output 3D face space is computed with the aid of material file (.mtl) and texture file (.jpg, .rgb) and viewed by OBJ viewer to achieve good 3D representation using this approach.
An Erratum for this chapter can be found at http://dx.doi.org/10.1007/978-3-642-25200-6_40
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Akaydın, A., Kucuktunc, O.: 3D Face Reconstruction from 2D Images for Effective Face Recognition, pp. 25–31 (2009)
Mena-Chalco, J.P., et al.: PCA- based 3D Face Photography. In: SIBGRAPI 2008 Proceedings of the XXI Brazilian Symposium on Computer Graphics and Image Processing, pp. 313–320 (2008)
Pentland, A., Sclaroff, S.: Closed-form solutions for physically based shape modeling and recognition. IEEE Pattern Analysis and Machine Intelligence 13(7), 715–729 (1991)
Hu, Y., Jiang, D., Yan, S., Zhang, L., Zhang, H.: Automatic 3D Reconstruction for Face Recognition. In: Proceedings. Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 843–848 (2004)
Duan, Y., Yang, L., Qin, H., Samaras, D.: Shape Reconstruction from 3D and 2D Data Using PDE-Based Deformable Surfaces. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 238–251. Springer, Heidelberg (2004)
Ansari, A.-N., Abdel-Mottaleb, M., Mahoor, M.H.: Disparity-Based 3D Face Modeling using 3D Deformable Facial Mask for 3D Face Recognition. In: 2006 IEEE International Conference on Multimedia and Expo., pp. 981–984 (2006)
Metaxas, D., Terzopoulos, D.: Shape and nonrigid motion estimation through physics-based synthesis. IEEE Pattern Analysis and Machine Intelligence 15(6), 580–591 (1993)
McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: a survey. Medical Image Analysis 1(2), 91–108 (1996)
Metaxas, D., Terzopoulos, D.: Physics-based modeling and reasoning in computer vision. Computer Vision and Image Understanding 65(5), 111–359
Slaroff, S., Pentland, A.P.: Modal Matching for correspondence and recognition. Journal IEEE Transactions on Pattern Analysis and Machine Intelligence 17(6), 308–313 (1995)
Wang, J., Yin, L., Wei, X., Sun, Y.: 3D Facial Expression Recognition Based on Primitive Surface Feature Distribution. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1399–1406 (2006)
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Chung, S.H., Khor, E.T. (2011). Retracted: Reconstruction of 3D Faces Using Face Space Coefficient, Texture Space and Shape Space. In: Zaman, H.B., et al. Visual Informatics: Sustaining Research and Innovations. IVIC 2011. Lecture Notes in Computer Science, vol 7067. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25200-6_12
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DOI: https://doi.org/10.1007/978-3-642-25200-6_12
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