Abstract :
In this paper, we propose a new approach for constructing a 3D morph able model (3DMM) and experiment its application to face recognition. Differently from existing solutions, the proposed 3DMM is constructed from a training set that includes a large spectrum of variability in terms of ethnicity and facial expressions. By exploiting annotated landmarks available in the training data, we are able of establishing dense correspondence across training scans also in the presence of strong facial expressions. The 3DMM is then constructed by learning a dictionary of basis components, instead of using the traditional approach based on PCA decomposition. Finally, we cast the proposed dictionary learning DL-3DMM to a rigid/non-rigid deformation framework, which includes pose estimation and regularized ridge-regression fitting to 2D images. Comparative results between the DL-3DMM and its PCA counterpart are reported, together with face recognition results for images with large pose and expression variations.
