YOLO-face: a real-time face detector - Chen, Weijun; Huang, Hongbo; Peng, Shuai; Zhou, Changsheng; Zhang, Cuiping (The Visual computer, 2021)
Face detection is one of the important tasks of object detection. Typically detection is the first stage of pattern recognition and identity authentication. In recent years, deep learning-based algorithms in object detection have grown rapidly. These algorithms can be generally divided into two categories, i.e., two-stage detector like Faster R-CNN and one-stage detector like YOLO. Although YOLO and its varieties are not so good as two-stage detectors in terms of accuracy, they outperform the counterparts by a large margin in speed. YOLO performs well when facing normal size objects, but is incapable of detecting small objects. The accuracy decreases notably when dealing with objects that have large-scale changing like faces. Aimed to solve the detection problem of varying face scales, we propose a face detector named YOLO-face based on YOLOv3 to improve the performance for face detection. The present approach includes using anchor boxes more appropriate for face detection and a more precise regression loss function. The improved detector significantly increased accuracy while remaining fast detection speed. Experiments on the WIDER FACE and the FDDB datasets show that our improved algorithm outperforms YOLO and its varieties.