@inproceedings{64f49ebce2aa4045b078f9fc8b4eb4f8,
title = "Strengthening surf descriptor with discriminant image filter learning: Application to face recognition",
abstract = "Face recognition in extreme situations is still challenging to researchers. While several algorithms have shown great recognition results in ideal conditions, accuracy decreases when recognition tasks present a high illumination variation. In this paper, we propose to add two components to the recognition system to make the surf descriptor efficient in such extreme situations. First, we learn a discriminant image filter that maximizes the discrimination of surf. Second, the obtained discriminant surf (d-surf) is further strengthened by using multispectral images instead of broad band images. DSURF and multispectral d-surf (MD-SURF) were evaluated against two face databases: the feret database, which served as a benchmark, and the iris-m3 multispectral face database, which presented sun lighted faces. Our algorithms have been evaluated against three state-of-the-art algorithms that are MBLBP, HGPP and LGBPHS. The results validated the superiority of D-SURF over the traditional surf descriptor, while MD-SURF performed best out of all studied algorithms.",
keywords = "FERET, Face, HGPP, IRIS-M, LGBPHS, MBLBP, SURF, filter, illumination, multispectral",
author = "Hamdi Bouchech and Sebti Foufou and Mongi Abidi",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 26th International Conference on Microelectronics, ICM 2014 ; Conference date: 14-12-2014 Through 17-12-2014",
year = "2014",
doi = "10.1109/ICM.2014.7071825",
language = "English (US)",
series = "Proceedings of the International Conference on Microelectronics, ICM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "136--139",
booktitle = "2014 26th International Conference on Microelectronics, ICM 2014",
}