  Occlusion is a common problem with biometric recognition in the wild. The
generalization ability of CNNs greatly decreases due to the adverse effects of
various occlusions. To this end, we propose a novel unified framework
integrating the merits of both CNNs and graph models to overcome occlusion
problems in biometric recognition, called multiscale dynamic graph
representation (MS-DGR). More specifically, a group of deep features reflected
on certain subregions is recrafted into a feature graph (FG). Each node inside
the FG is deemed to characterize a specific local region of the input sample,
and the edges imply the co-occurrence of non-occluded regions. By analyzing the
similarities of the node representations and measuring the topological
structures stored in the adjacent matrix, the proposed framework leverages
dynamic graph matching to judiciously discard the nodes corresponding to the
occluded parts. The multiscale strategy is further incorporated to attain more
diverse nodes representing regions of various sizes. Furthermore, the proposed
framework exhibits a more illustrative and reasonable inference by showing the
paired nodes. Extensive experiments demonstrate the superiority of the proposed
framework, which boosts the accuracy in both natural and occlusion-simulated
cases by a large margin compared with that of baseline methods.
