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Occlusion is a common problem with biometric recognition in the wild. Thegeneralization ability of CNNs greatly decreases due to the adverse effects ofvarious occlusions. To this end, we propose a novel unified frameworkintegrating the merits of both CNNs and graph models to overcome occlusionproblems in biometric recognition, called multiscale dynamic graphrepresentation (MS-DGR). More specifically, a group of deep features reflectedon certain subregions is recrafted into a feature graph (FG). Each node insidethe 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 thesimilarities of the node representations and measuring the topologicalstructures stored in the adjacent matrix, the proposed framework leveragesdynamic graph matching to judiciously discard the nodes corresponding to theoccluded parts. The multiscale strategy is further incorporated to attain morediverse nodes representing regions of various sizes. Furthermore, the proposedframework exhibits a more illustrative and reasonable inference by showing thepaired nodes. Extensive experiments demonstrate the superiority of the proposedframework, which boosts the accuracy in both natural and occlusion-simulatedcases by a large margin compared with that of baseline methods.

Multiscale Dynamic Graph Representation for Biometric Recognition with Occlusions