Image-based facial identity recognition has become a technology that is now used in many applications. This is because it is possible to use only a camera without the need for any other device. Besides, due to the advantage of contactless technology, it is one of the most popular certifications. However, a common recognition system is not possible if some of the face information is lost due to the user’s posture or the wearing of masks, as caused by the recent prevalent disease. In some platforms, although performance is improved through incremental updates, it is still inconvenient and inaccurate. In this paper, we propose a method to respond more actively to these situations. First, we determine whether an obscurity occurs and improve the stability by calculating the feature vector using only a significant area when the obscurity occurs. By recycling the existing recognition model, without incurring little additional costs, the results of reducing the recognition performance drop in certain situations were confirmed. Using this technique, we confirmed a performance improvement of about 1~3% in a situation where some information is lost. Although the performance is not dramatically improved, it has the big advantage that it can improve recognition performance by utilizing existing systems.

中文翻译：

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基于遮挡检测的特征选择改善身份识别

基于图像的面部身份识别已成为一种如今已在许多应用程序中使用的技术。这是因为可以仅使用相机而无需任何其他设备。此外，由于非接触技术的优势，它是最受欢迎的认证之一。然而，如果由于最近的流行病引起的由于用户的姿势或戴口罩而丢失了一些面部信息，则通用的识别系统是不可能的。在某些平台上，尽管通过增量更新提高了性能，但仍然不方便且不准确。在本文中，我们提出了一种更积极地应对这些情况的方法。首先，我们确定是否发生遮挡，并通过在发生遮挡时仅使用有效面积来计算特征向量，从而提高稳定性。通过回收现有的识别模型，而又不增加额外成本，可以确认在某些情况下降低识别性能下降的结果。使用这种技术，我们确认了在丢失某些信息的情况下性能可提高约1〜3％。尽管性能没有显着提高，但是它具有很大的优势，即可以通过利用现有系统来提高识别性能。