Robust virtual frontal face synthesis from a given pose using regularized linear regression

Locally linear regression (LLR) is a simple yet efficient algorithm for synthesizing a virtual frontal image from a nonfrontal viewpoint. However, the effect of LLR is impacted by the patch size. Moreover, for the adjacent patches of the nose and mouth part, different local linear mappings cannot guarantee the predicted virtual frontal patches to be harmonious. The major cause is the different local geometric shape for different person. To overcome or at least to reduce the problem of LLR, we propose a regularization framework by introducing a global regularization item into the original local regression object function. The reconstruction weights are estimated through the new model and the virtual frontal face are predicted using the weights. Experimental results show that the method performs better than the LLR.