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Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):

M. Hörhan, H. Eidenberger:
"Gestalt Interest Points for Image Description in Weight-Invariant Face Recognition";
Vortrag: SPIE Visual Communications and Image Processing Conference, Paris, FR; 17.09.2014 - 20.09.2014; in: "SPIE Visual Communications Proceedings", SPIE, (2014).



Kurzfassung deutsch:
In this work, we propose two improvements of the Gestalt Interest Points (GIP) algorithm for the recognition
of faces of people that have underwent signi cant 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 e cient while retaining our classi cation results. To nd 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.

Kurzfassung englisch:
In this work, we propose two improvements of the Gestalt Interest Points (GIP) algorithm for the recognition
of faces of people that have underwent signi cant 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 e cient while retaining our classi cation results. To nd 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.

Schlagworte:
Face recognition, person identi cation, content-based image analysis, local interest point detection

Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.