Human face 3D reconstruction with handheld single 2D camera on mobile devices

Author(s):  
Alexander Limonov ◽  
Jae-Yun Jeong ◽  
Minsoo Kim ◽  
Sungjin Kim ◽  
Yonggyoo Kim
Author(s):  
Kiran K. Vupparaboina ◽  
Roopak R. Tamboli ◽  
Shanmukh Manne ◽  
Peter A. Kara ◽  
Maria G. Martini ◽  
...  

2012 ◽  
Vol 157-158 ◽  
pp. 1008-1011
Author(s):  
Hui Huang Zhao ◽  
Yao Nan Wang ◽  
Ya Qi Sun ◽  
Jian Zhen Chen

Human face three-dimensional (3D) reconstruction is a challenging problem. In this paper, we propose a human face fast- 3D- reconstruction method based on image processing with a single image. Shape from shading (SFS) is chosen to reconstruct the human face. First, SFS theory is introduced. It has the advantage of fast 3D reconstruction and only need a single image. Secondly, because the noise will affect the 3D reconstruction result greatly, wavelet transform and wavelet packet transform are introduced and used in image denoising respectively. The experiment has shown that the method based on wavelet transform produces the best denoising result than wavelet packet transform. At last, a human face 3D reconstruction algorithm based on a single image is proposed. The experimental results show that a human face 3D model can be reconstructed in fast by proposed algorithm.


Author(s):  
A. Masiero ◽  
F. Fissore ◽  
M. Piragnolo ◽  
A. Guarnieri ◽  
F. Pirotti ◽  
...  

<p><strong>Abstract.</strong> The Worldwide spread of relatively low cost mobile devices embedded with dual rear cameras enables the possibility of exploiting smartphone stereo vision for producing 3D models. Despite such idea is quite attractive, the small baseline between the two cameras restricts the depth discrimination ability of this kind of stereo vision systems. This paper presents the results obtained with a smartphone stereo vision system by using two rear cameras with different focal length: this operating condition clearly reduces the matchable area. Nevertheless, 3D reconstruction is still possible and the obtained results are evaluated for several camera-object distances.</p>


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