Accurate Face Detection for High Performance
This work addresses face detection, particularly for tiny faces, which is important for applications like surveillance and photography, but it is incremental as it builds on existing methods.
The paper tackled the problem of improving detection performance for tiny faces by applying recent tricks to the RetinaNet approach, achieving state-of-the-art performance on the WIDER FACE dataset.
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.