Abstract
Object detection has been a hot research topic for many years to date. Among various task we have achieved so far, recent state-of-the-art face detection through deep learning architecture method have proven to be very robust to many kinds of image scenario. Yet when it comes to very tiny faces, typically faces that has no more than 10~20 in pixel sizes, still remains a challenging task for face detection. Many methods have been proposed to address tiny face detection problem. Most of them are based on image-pyramid or feature-pyramid method. By leveraging multiple sizes in spatial domain or receptive field in convolutional feature layers, it’s a common believe that we can extract more information and thus getting a better result in face detection. Both of these method have their pros and cons. In this paper, we aim our goal to the tiny face detection and introduce a method that utilize super resolution technique which can increase the sample candidate from the existing detection pipeline and enhance the performance without additional image or feature pyramids. We provided detailed comparison and visualization in our work, and the result have shown performance boost in face detection task.