Adaptive face detection algorithms in blurring scenarios

Face detection is a very important process in facial image processing. There are many existing face detection algorithms, however, we observe that there are still a lot of room for improvement in the blurred scenario, since blurred faces have much fewer meaningful features compared to clear ones. In this work, we propose a detection framework for blurred faces using several image processing techniques. First, multiple facial images with different extents and approaches of blurriness are generated for the training and validation sets. With them, several neural network models with different architectures, including YOLO and the DenseNet, are trained. Finally, some geometric and color relationships are examined in order to eliminate the redundant face candidates. Moreover, we also conduct an experiment that involves ensemble learning. The experimental results show that our method is superior to the state-of-the-art face detection methods in dealing with blurred faces, and we can boost the overall performance for face detection effectively.