Heterogeneous Face Recognition (HFR) is a task that matches faces across two different domains such as visible light (VIS), near-infrared (NIR), or the sketch domain. Due to the lack of databases, HFR methods usually exploit the pre-trained features on a large-scale visual database that contain general facial information. However, these pre-trained features cause performance degradation due to the texture discrepancy with the visual domain. With this motivation, we propose a graph-structured module called Relational Graph Module (RGM) that extracts global relational information in addition to general facial features. Because each identity’s relational information between intra-facial parts is similar in any modality, the modeling relationship between features can help cross-domain matching. Through the RGM, relation propagation diminishes texture dependency without losing its advantages from the pre-trained features. Furthermore, the RGM captures global facial geometrics from locally correlated convolutional features to identify long-range relationships. In addition, we propose a Node Attention Unit (NAU) that performs node-wise recalibration to concentrate on the more informative nodes arising from relation-based propagation. Furthermore, we suggest a novel conditional-margin loss function ( CC -softmax) for the efficient projection learning of the embedding vector in HFR. The proposed method outperforms other state-of-the-art methods on five HFR databases. Furthermore, we demonstrate performance improvement on three backbones because our module can be plugged into any pre-trained face recognition backbone to overcome the limitations of a small HFR database.

中文翻译：

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用于异构人脸识别的关系深度特征学习


异构人脸识别 (HFR) 是一项跨两个不同域（例如可见光 (VIS)、近红外 (NIR) 或草图域）匹配人脸的任务。由于缺乏数据库，HFR 方法通常利用包含一般面部信息的大规模视觉数据库上的预训练特征。然而，由于纹理与视觉域的差异，这些预先训练的特征会导致性能下降。出于这个动机，我们提出了一种称为关系图模块（RGM）的图结构模块，除了一般面部特征之外，它还提取全局关系信息。由于每个身份的人脸各部分之间的关​​系信息在任何模态下都是相似的，因此特征之间的建模关系可以帮助跨域匹配。通过 RGM，关系传播减少了纹理依赖性，同时又不失去预训练特征的优势。此外，RGM 从局部相关的卷积特征中捕获全局面部几何形状，以识别远程关系。此外，我们提出了一个节点注意单元（NAU），它执行节点方式的重新校准，以集中于基于关系的传播产生的信息更丰富的节点。此外，我们提出了一种新的条件裕度损失函数（CC -softmax），用于 HFR 中嵌入向量的有效投影学习。该方法在五个 HFR 数据库上优于其他最先进的方法。此外，我们展示了三个主干网的性能改进，因为我们的模块可以插入任何预先训练的人脸识别主干网，以克服小型 HFR 数据库的限制。