Dealing with occlusions in face recognition by region-based fusion

The last research efforts made in the face recognition community have been focusing in improving the robustness of systems under different variability conditions like change of pose, expression, illumination, low resolution and occlusions. Occlusions are also a manner of evading identification, which is commonly used when committing crimes or thefts. In this work we propose an approach based on the fusion of non occluded facial regions that is robust to occlusions in a simple and effective manner. We evaluate the region-based approach in three face recognition systems: Face++ (a commercial software based on CNN) and two advancements over LBP systems, one considering multiple scales and other considering a larger number of facial regions. We report experiments based on the ARFace database and prove the robustness of using only non-occluded facial regions, the effectiveness of a large number of regions and the limitations of the commercial system when dealing with occlusions.