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.

翻译：遮挡是野外生物识别中的常见问题。由于各种遮挡的不利影响，卷积神经网络（CNNs）的泛化能力大幅下降。为此，我们提出了一种新颖的统一框架，融合了CNNs和图模型的优点，以克服生物识别中的遮挡问题，称为多尺度动态图表示（MS-DGR）。具体来说，将一组反映在特定子区域上的深层特征重构为特征图（FG）。FG中的每个节点被认为是对输入样本特定局部区域的表征，而边则表示非遮挡区域的共现关系。通过分析节点表示的相似性并测量邻接矩阵中存储的拓扑结构，所提出的框架利用动态图匹配来明智地丢弃对应于遮挡部分的节点。进一步引入多尺度策略，以获得代表不同大小区域的更多样化节点。此外，通过显示配对的节点，所提出的框架展现出更具说明性和合理性的推理过程。大量实验证明了所提出框架的优越性，与基线方法相比，其在自然场景和模拟遮挡场景下的准确性均有大幅提升。