Face detection has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs). Its central issue in recent years is how to improve the detection performance of tiny faces. To this end, many recent works propose some specific strategies, redesign the architecture and introduce new loss functions for tiny object detection. In this report, we start from the popular one-stage RetinaNet approach and apply some recent tricks to obtain a high performance face detector. Specifically, we apply the Intersection over Union (IoU) loss function for regression, employ the two-step classification and regression for detection, revisit the data augmentation based on data-anchor-sampling for training, utilize the max-out operation for classification and use the multi-scale testing strategy for inference. As a consequence, the proposed face detection method achieves state-of-the-art performance on the most popular and challenging face detection benchmark WIDER FACE dataset.

本篇研究通过Intersection over Union (IoU)回归的损失函数，两步分类和回归的检测方法，数据基于数据锚采样的增强，采用分类的最大输出操作，并且采用多尺度的测试策略在一个阶段的RetinaNet方法中应用一些技巧，从而获得高性能的人脸检测器。实验表明该算法在WIDER FACE数据集上的表现优于现有算法。

高性能精准人脸检测