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Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):

M. Hörhan, H. Eidenberger:
"Gestalt Interest Points with a Neural Network for Makeup-Robust Face Recognition";
Vortrag: 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece; 07.10.2018 - 10.10.2018; in: "2018 25th IEEE International Conference on Image Processing (ICIP)", IEEE Press, (2018), ISSN: 2381-8549; S. 2391 - 2395.



Kurzfassung deutsch:
In this paper, we propose a novel approach for the domain of makeup-robust face recognition. Most face recognition schemes usually fail to generalize well on these data where there is a large difference between the training and testing sets, e.g., makeup changes. Our method focuses on the problem of determining whether face images before and after makeup refer to the same identity. The work on this fundamental research topic benefits various real-world applications, for example automated passport control, security in general, and surveillance. Experiments show that our method is highly effective in comparison to state-of-the-art methods.

Kurzfassung englisch:
In this paper, we propose a novel approach for the domain of makeup-robust face recognition. Most face recognition schemes usually fail to generalize well on these data where there is a large difference between the training and testing sets, e.g., makeup changes. Our method focuses on the problem of determining whether face images before and after makeup refer to the same identity. The work on this fundamental research topic benefits various real-world applications, for example automated passport control, security in general, and surveillance. Experiments show that our method is highly effective in comparison to state-of-the-art methods.

Schlagworte:
Deep Learning, Feature Engineering, Gestalt Properties


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/ICIP.2018.8451075

Elektronische Version der Publikation:
https://ieeexplore.ieee.org/abstract/document/8451075


Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.