  360-degree cameras offer the possibility to cover a large area, for example
an entire room, without using multiple distributed vision sensors. However,
geometric distortions introduced by their lenses make computer vision problems
more challenging. In this paper we address face detection in 360-degree fisheye
images. We show how a face detector trained on regular images can be re-trained
for this purpose, and we also provide a 360-degree fisheye-like version of the
popular FDDB face detection dataset, which we call FDDB-360.
