Resolution-aware Constrained Local Model with mixture of local experts

Deformable model fitting to high-resolution facial images has been extensively studied for over two decades. However, due to the ill-posed problem caused by low-resolution images, most existing work cannot be applied directly and degrades quickly as the resolution decreases. To address this issue, this paper extends the Constrained Local Model (CLM) to a multi-resolution model consisting of a 4-level patch pyramid, and deploys various feature descriptors for the local patch experts as well. We evaluate the proposed work on the BioID, the MUCT and the Multi-PIE datasets. Superior results are achieved on almost all resolution levels, demonstrating the effectiveness and necessity of our resolution-aware approach for the low-resolution fitting. Improved performance of patch models employing several feature combinations over the single intensity feature under different conditions is also presented.