TY - GEN
T1 - FaRE
T2 - 26th IEEE International Conference on Image Processing, ICIP 2019
AU - Xu, Xiang
AU - Kakadiaris, Ioannis A.
N1 - Funding Information:
Acknowledgment This material is supported by the U.S. Department of Homeland Security under Grant Award Number 2017-ST-BTI-0001-0201. 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 “EDGE” 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. This work was completed in part with resources provided by the Core facility for Advanced Computing and Data Science at the University of Houston.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Biometrics-related research has been accelerated significantly by deep learning technology. However, there are limited open-source resources to help researchers evaluate their deep learning-based biometrics algorithms efficiently, especially for the face recognition tasks. In this work, we design, implement, and evaluate a computationally lightweight, maintainable, scalable, generalizable, and extendable face recognition evaluation toolbox named FaRE that supports both online and offline evaluation to provide feedback to algorithm development and accelerate biometricsrelated research. FaRE includes a set of evaluation metrics and provides various APIs for commonly-used face recognition datasets including LFW, CFP, UHDB31, and IJBseries datasets. FaRE can be easily extended to include other datasets. The package is publically available for research use at https://github.com/uh-cbl/FaRE.
AB - Biometrics-related research has been accelerated significantly by deep learning technology. However, there are limited open-source resources to help researchers evaluate their deep learning-based biometrics algorithms efficiently, especially for the face recognition tasks. In this work, we design, implement, and evaluate a computationally lightweight, maintainable, scalable, generalizable, and extendable face recognition evaluation toolbox named FaRE that supports both online and offline evaluation to provide feedback to algorithm development and accelerate biometricsrelated research. FaRE includes a set of evaluation metrics and provides various APIs for commonly-used face recognition datasets including LFW, CFP, UHDB31, and IJBseries datasets. FaRE can be easily extended to include other datasets. The package is publically available for research use at https://github.com/uh-cbl/FaRE.
KW - Evaluation
KW - Face Recognition
KW - Toolbox
UR - http://www.scopus.com/inward/record.url?scp=85076814744&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076814744&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2019.8803411
DO - 10.1109/ICIP.2019.8803411
M3 - Conference contribution
AN - SCOPUS:85076814744
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 3272
EP - 3276
BT - 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 September 2019 through 25 September 2019
ER -