发表机构
UIT（UIT）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对口罩遮挡下面部识别准确率下降问题，提出MGFace掩码门控面部识别管道，通过预测掩码状态有条件地路由相似性计算，区分不同情况处理，实验表明其能高效提升准确率，降低查询时间。
AI 中文摘要
面部识别在正常条件下取得了显著性能。然而，当查询面部部分遮挡（尤其是被口罩遮挡）时，其准确性往往会显著下降。现有重新排序方法通过利用补丁级相似性提高鲁棒性，但常依赖成本高、细粒度的匹配机制，限制了大规模检索场景下的效率。本文提出MGFace，一种掩码门控面部识别管道，预测查询面部的掩码状态并相应地有条件地路由相似性计算。具体而言，MGFace区分掩码和未掩码查询，对未掩码查询应用全局嵌入匹配，仅对掩码查询激活掩码感知补丁级重新排序。该设计专注于可靠的上脸区域，避免不必要的细粒度计算。在扩展的LFW-Mask数据集上的实验表明，使用FaceNet主干时MGFace实现了超过80%的识别准确率，使用ArcFace主干时超过90%。与之前基于EMD的重新排序方法相比，MGFace在降低查询时间约20倍的同时实现了更好的识别性能。这些结果证明了MGFace在以低计算开销提高掩码面部识别准确率方面的有效性。源代码可在指定网址获取。
英文摘要
Face identification has achieved remarkable performance under normal conditions. Yet, its accuracy often degrades significantly when query faces are partially occluded, especially by facial masks. Existing re-ranking approaches improve robustness by exploiting patch-level similarities. Still, they often rely on costly, fine-grained matching mechanisms, which limit their efficiency in large-scale retrieval scenarios. In this paper, we propose MGFace, a mask-gated face identification pipeline that predicts the mask status of a query face and conditionally routes the similarity computation accordingly. Specifically, MGFace distinguishes between masked and unmasked queries, applies global embedding matching to unmasked queries, and activates mask-aware patch-level re-ranking only for masked queries. This design focuses on reliable upper-face regions while avoiding unnecessary fine-grained computation. Experiments on the extended LFW-Mask dataset show that MGFace achieves over 80% identification accuracy with the FaceNet backbone and over 90% with the ArcFace backbone. Compared with a previous EMD-based re-ranking method, MGFace achieves better identification performance while reducing query time by approximately 20x. These results demonstrate the effectiveness of MGFace in improving masked-face identification accuracy with low computational overhead. The source code is available at https://github.com/chequanghuy/MGFace.
