ABSTRACT
PSNR test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 13.4 Generalization capability of set estimation method . . . . . . . . . . . . . 283 13.5 Test of significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285
Synthetic generation of face images for the purpose of face recognition has been explored in recent times. Two dimensional (2D) to three-dimensional (3D) reconstruction and generation of new face images of various shapes and appearances had received attention. The popular solution to the problem is proposed by 2D and 3D modeling of faces. 3D models include face mesh frames, morphable models and depth map-based models, where one needs to incorporate high quality graphics and complex animation algorithms. Flynn et al. [85] provided a survey of approaches and challenges in 3D, multimodal 3D and 2D face recognition. 3D head poses are derived from 2D to 3D feature correspondences [86]. Face recognition based on fitting a 3D morphable model is also proposed with statistical texture [87]. Some significant works are discussed in Table 2.5. Four main approaches for 2D modeling are active appearance models (AAMs) [80], manifolds [210], geometry-driven face synthesis methods [68] including face animation[82] and expression mapping techniques [108],[84].
