SARS-CoV-2 has presented direct and indirect challenges to the scientific community. One of the most prominent indirect challenges advents from the mandatory use of face masks in a large number of countries. Face recognition methods struggle to perform identity verification with similar accuracy on masked and unmasked individuals. It has been shown that the performance of these methods drops considerably in the presence of face masks, especially if the reference image is unmasked. We propose FocusFace, a multi-task architecture that uses contrastive learning to be able to accurately perform masked face recognition. The proposed architecture is designed to be trained from scratch or to work on top of state-of-the-art face recognition methods without sacrificing the capabilities of a existing models in conventional face recognition tasks. We also explore different approaches to design the contrastive learning module. Results are presented in terms of masked-masked (M-M) and unmasked-masked (U-M) face verification performance. For both settings, the results are on par with published methods, but for M-M specifically, the proposed method was able to outperform all the solutions that it was compared to. We further show that when using our method on top of already existing methods the training computational costs decrease significantly while retaining similar performances. The implementation and the trained models are available at GitHub.
翻译:SARS-COV-2号卫星对科学界提出了直接和间接的挑战。最突出的间接挑战之一是,许多国家强制使用面罩,这是其中最突出的间接挑战之一。 面部识别方法在对面罩和无面罩的个人进行类似精确的身份验证方面挣扎,显示这些方法的性能在面罩上显著下降,特别是当参考图像被揭开时。我们提议FocusFace(一个多任务结构,即使用对比学习来准确进行面部识别,从而能够准确进行面部识别。拟议结构的设计是要从零开始或在最先进的面部识别方法之上进行训练,而不是牺牲现有模型在常规面部识别任务上的能力。我们还探索了设计对比学习模块的不同方法。这些方法的表现表现在面罩(M-M)和无面罩(U-M)的核查性能方面。在两种情况下,结果与公布的方法相同,但具体来说,M-M-M(M),拟议方法的设计能够超越所有与最新面部识别方法相比,而不是牺牲现有模型的能力。我们所培训的目前采用的方法在进行同样的计算时,在使用的最高成本时,我们已经保持了现有的业绩。我们用的方法。