FDDB-360: Face Detection in 360-degree Fisheye Images
This addresses face detection in distorted 360-degree camera images, which is incremental as it adapts existing methods to a new domain.
The paper tackles face detection in 360-degree fisheye images by retraining a face detector on regular images and introduces FDDB-360, a dataset adapted from FDDB for this purpose.
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.