3D facial shape reconstruction using macro- and micro-level features from high resolution facial images

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.