Detecting Arbitrarily Rotated Faces for Face Analysis
Frerk Saxen, Sebastian Handrich, Philipp Werner, Ehsan Othman, Ayoub K. Al-Hamadi · 2019
Current face detection concentrates on detecting tiny faces and severely occluded faces. Face analysis methods, however, require a good localization and would benefit greatly from some rotation information. We propose to predict a face direction vector (FDV), which provides the face size and orientation and can be learned by a common object detection architecture better than the traditional bounding box. It provides a more consistent definition of face location and size. Using the FDV is promising for all succeeding face analysis methods. As an example, we show that facial landmark detection can highly benefit from pre-aligned faces.