[Zurück]

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.

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.

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.

Deep Learning, Feature Engineering, Gestalt Properties

http://dx.doi.org/10.1109/ICIP.2018.8451075

https://ieeexplore.ieee.org/abstract/document/8451075