Gestalt interest points for image description in weight-invariant face recognition
Markus Hörhan, Horst Eidenberger · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
In this work, we propose two improvements of the Gestalt Interest Points (GIP) algorithm for the recognition of faces of people that have underwent significant weight change. The basic assumption is that some interest points contribute more to the description of such objects than others. We assume that we can eliminate certain interest points to make the whole method more efficient while retaining our classification results. To find out which gestalt interest points can be eliminated, we did experiments concerning contrast and orientation of face features. Furthermore, we investigated the robustness of GIP against image rotation. The experiments show that our method is rotational invariant and - in this practically relevant forensic domain - outperforms the state-of-the-art methods such as SIFT, SURF, ORB and FREAK.