A probabilistic union approach to robust face recognition with partial distortion and occlusion
Jie Lin, Ji Ming, Danny Crookes · 2008
This paper presents a new approach to face recognition where the images are subject to unknown, partial distortion/occlusion. The new approach is a probabilistic decision-based neural network (PDBNN), built on a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched local features and focusing the recognition mainly on the matched local features. It thereby improves the robustness while assuming no prior information about the corruption. We call the new approach the posterior union decision-based neural network (PUDBNN). The new PUDBNN has been evaluated on two face image databases, XM2VTS and ORL, using testing images subjected to various types of partial distortion and occlusion. The new system has demonstrated improved performance over other systems.