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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

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Three-dimensional face reconstruction from a single image by a coupled RBF network.

Mingli Song¹, Dacheng Tao, Xiaoqin Huang

¹College of Computer Science and Technology, Zhejiang University, Hangzhou, China.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|April 20, 2012

Summary

This study introduces a novel Coupled Radial Basis Function Network (C-RBF) for reconstructing 3-D face models from single 2-D images. The method effectively recovers 3-D facial geometry, crucial for applications like face recognition and animation.

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Area of Science:

Computer Vision
Machine Learning
3D Reconstruction

Background:

3-D face model reconstruction from 2-D images is vital for face recognition and animation.
Existing methods face challenges with viewpoint, illumination, and occlusion variations.

Purpose of the Study:

To develop a novel Coupled Radial Basis Function Network (C-RBF) for accurate 3-D face model recovery from single 2-D images.
To explore intrinsic representations of 2-D and 3-D faces and their mappings.

Main Methods:

Training a C-RBF network on a coupled dataset of 2-D and 3-D faces.
Utilizing intrinsic representations and mappings between 2-D and 3-D face data.
Assuming identical linear combination coefficients for 2-D and 3-D reconstruction.

Main Results:

The C-RBF network successfully reconstructs 3-D face models from single 2-D images.
Experimental results on the BU3D database demonstrate the method's effectiveness.
The approach handles variations in viewpoint and illumination implicitly.

Conclusions:

The proposed C-RBF network is an effective method for 3-D face model reconstruction.
This technique offers robust solutions for face recognition and animation applications.
The study highlights the potential of coupled network architectures in 3-D vision tasks.