Perspective Distortion Modeling in Face Images and Object Tracking Library
In a first chapter we describe a method to model perspective distortion as a one- parameter family of warping functions. This can be used to mitigate its effects on visual recognition, or interactively manipulate the perceived personality. The warps are learned from a novel face dataset and, by comparing orbits spanned by images instead of images themselves, we improve face recognition when small focal lengths are used. Additional applications are presented to image editing, videoconference, and multi-view validation of recognition systems.
A second chapter is devoted to a new versatile and modular open-source cross- platform online object tracking library, designed to be easily usable by the vision community. Object tracking plays a central part in a number of vision problems, and there is no, to date, a ready-to-use and extensible tracking library at the object level.