Gestalt interest points for image description in weight-invariant face recognition

Markus Hörhan; Horst Eidenberger

doi:10.1117/12.2178951

4 March 2015 Gestalt interest points for image description in weight-invariant face recognition

Markus Hörhan, Horst Eidenberger

Proceedings Volume 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014); 944307 (2015) https://doi.org/10.1117/12.2178951
Event: Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2014, Beijing, China

Abstract

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.

Citation Download Citation

Markus Hörhan and Horst Eidenberger "Gestalt interest points for image description in weight-invariant face recognition", Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 944307 (4 March 2015); https://doi.org/10.1117/12.2178951

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