Multiscale Dynamic Graph Representation for Biometric Recognition with Occlusions

TL;DR

This paper introduces a multiscale dynamic graph framework combining CNNs and graph models to improve biometric recognition accuracy under occlusions by effectively identifying and discarding occluded regions.

Contribution

The novel MS-DGR framework integrates multiscale graph representations with dynamic matching to handle occlusions in biometric recognition tasks.

Findings

Significantly improves recognition accuracy in occluded scenarios

Outperforms baseline methods in natural and simulated occlusion cases

Demonstrates robustness and generalization in biometric recognition

Abstract

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.