A Multi-Scale Cascade Fully Convolutional Network Face Detector
This work addresses face detection for computer vision applications, presenting an incremental improvement over existing methods.
The paper tackles the challenge of detecting faces at arbitrary locations and scales by proposing a three-stage cascade of fully convolutional networks that progressively refines face proposals, achieving strong performance on public datasets.
Face detection is challenging as faces in images could be present at arbitrary locations and in different scales. We propose a three-stage cascade structure based on fully convolutional neural networks (FCNs). It first proposes the approximate locations where the faces may be, then aims to find the accurate location by zooming on to the faces. Each level of the FCN cascade is a multi-scale fully-convolutional network, which generates scores at different locations and in different scales. A score map is generated after each FCN stage. Probable regions of face are selected and fed to the next stage. The number of proposals is decreased after each level, and the areas of regions are decreased to more precisely fit the face. Compared to passing proposals directly between stages, passing probable regions can decrease the number of proposals and reduce the cases where first stage doesn't propose good bounding boxes. We show that by using FCN and score map, the FCN cascade face detector can achieve strong performance on public datasets.