A Three-Category Face Detector With Contextual Information On Finding Tiny Faces

Great progresses have been achieved on object detection in the wild. However, it still remains a challenging problem due to tiny objects. In this paper, we present a Three-category Classification Neural Network to find tiny faces under complex environments by leveraging contextual information around faces. Tiny faces (within 20x20 pixels) are so fuzzy that the facial patterns are not clear or even ambiguous for detection. To solve this problem, instead of formulating the face detection as a two-category classification task, a novel face detection network is proposed for three-category classification, i.e., normal face, tiny face and background. Moreover, we take full advantage of contextual information around faces and pick good prior anchors to predict good detection on tiny faces. Extensive experiments on two challenging face detection benchmarks, FDDB and WIDER FACE, demonstrate the effectiveness of our method.