Abstract
CNN-based face detection methods have achieved significant progress in recent years. In addition to the strong representation ability of CNN, post-processing methods are also very important for the performance of face detection. In general, the face detection method predicts several candidate bounding-boxes for one face. NMS is used to filter out inaccurate candidate boxes to get the most accurate box. The principle of NMS is to select the box with a higher score as the basic box and then delete the box which has a large overlapping area with the basic box but has a lower score. However, the current NMS method and its improved versions do not perform well when face image quality is poor or faces are in a cluster. In these situations, even after NMS filtering, there is often a face corresponding to multiple predicted boxes. To reduce this kind of negative result, in this paper, we propose a new NMS method that operates in the reverse order of other NMS methods. Our method performs well on low-quality and tiny face samples. Experiments demonstrate that our method is effective as a post-processor for different face detection methods.
Abstract (translated)
卷积神经网络(CNN)为基础的面部检测方法在过去几年中取得了显著进展。除了CNN的强大表示能力之外，预处理方法对于面部检测的性能也非常重要。通常，面部检测方法预测多个面部的候选框。NMS被用来过滤掉不准确的候选框，以得到最准确的框。NMS的原理是选择得分更高的框作为基本框，然后删除得分较低的框。然而，当面部图像质量不好或面部处于集群中时，当前版本的NMS方法和改进版的NMS方法表现不好。在这些情况下，即使经过NMS过滤，仍然可能出现多个预测框对应着一个面部的情况。为了减少这种负面影响，在本文中，我们提出了一种新的NMS方法，其操作顺序与其他NMS方法相反。我们的方法和在低质量和小尺寸面部样本上表现良好的方法证明了我们的方法和不同的面部检测方法作为预处理方法的有效性。
URL
https://arxiv.org/abs/2305.10593
