Three-dimensional (3D) facial modeling and stereo matching-based methods are widely used for 3D facial reconstruction from 2D single-view and multiple-view images. However, these methods cannot realistically reconstruct 3D faces because they use insufficient numbers of macro-level Facial Feature Points (FFPs). This paper proposes an accurate and person-specific 3D facial reconstruction method that uses ample numbers of macro- and micro-level FFPs to enable coverage of all facial regions of high resolution facial images. Comparisons of 3D facial images reconstructed using the proposed method for ground-truth 3D facial images from the Bosphorus 3D database show that the method is superior to a conventional Active Appearance Model-Structure from Motion (AAM + SfM)-based method in terms of average 3D root mean square error between the reconstructed and ground-truth 3D faces. Further, the proposed method achieved outstanding accuracy in local facial regions such as the cheek—areas where extraction of FFPs is difficult for existing methods.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition