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.

提出了一种名为多尺度动态图表示（MS-DGR）的新颖统一框架，通过结合卷积神经网络（CNNs）和图模型的优点，克服生物特征识别中遮挡问题。通过将某些子区域反映在深度特征图上并通过动态图匹配分析节点表示的相似性和存储在邻接矩阵中的拓扑结构，巧妙丢弃与遮挡部分对应的节点，从而实现更准确的推断结果。综合实验证明，与基线方法相比，所提出的框架在自然和模拟遮挡案例中均大幅提升了准确性。

多尺度动态图表示用于带有遮挡的生物识别