@inproceedings{7dd1fc9c8ebe486eb540fa33463ab815,
title = "Fully associative patch-based 1-to-N matcher for face recognition",
abstract = "This paper focuses on improving face recognition performance by a patch-based 1-to-N signature matcher that learns correlations between different facial patches. A Fully Associative Patch-based Signature Matcher (FAPSM) is proposed so that the local matching identity of each patch contributes to the global matching identities of all the patches. The proposed matcher consists of three steps. First, based on the signature, the local matching identity and the corresponding matching score of each patch are computed. Then, a fully associative weight matrix is learned to obtain the global matching identities and scores of all the patches. At last, the l1-regularized weighting is applied to combine the global matching identity of each patch and obtain a final matching identity. The proposed matcher has been integrated with the UR2D system for evaluation. The experimental results indicate that the proposed matcher achieves better performance than the current UR2D system. The Rank-1 accuracy is improved significantly by 3% and 0.55% on the UHDB31 dataset and the IJB-A dataset, respectively.",
keywords = "Face recognition, Fully associative, Matcher",
author = "Lingfeng Zhang and Ioannis Kakadiaris",
note = "Funding Information: This material is based upon work supported by the U.S. Department of Homeland Security under Grant Award Number 2015-ST-061-BSH001. This grant is awarded to the Borders, Trade, and Immigration (BTI) Institute: A DHS Center of Excellence led by the University of Houston, and includes support for the project “Image and Video Person Identification in an Operational Environment: Phase I” awarded to the University of Houston. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security. Publisher Copyright: {\textcopyright} 2018 IEEE.; 11th IAPR International Conference on Biometrics, ICB 2018 ; Conference date: 20-02-2018 Through 23-02-2018",
year = "2018",
month = jul,
day = "13",
doi = "10.1109/ICB2018.2018.00032",
language = "English (US)",
series = "Proceedings - 2018 International Conference on Biometrics, ICB 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "148--157",
booktitle = "Proceedings - 2018 International Conference on Biometrics, ICB 2018",
address = "United States",
}