TY - GEN
T1 - UHDB31
T2 - 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
AU - Le, Ha A.
AU - Kakadiaris, Ioannis A.
N1 - 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” 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:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Face datasets are a fundamental tool to analyze the performance of face recognition algorithms. However, the accuracy achieved on current benchmark datasets is saturated. Although multiple face datasets have been published recently, they only focus on the number of samples and lack diversity on facial appearance factors, such as pose and illumination. In addition, while 3D data have been demonstrated improved face recognition accuracy by a significant margin, only a few 3D face datasets provide high quality 2D and 3D data. In this paper, we introduce a new and challenging dataset, called UHDB31, which not only allows direct measurement of the influence of pose, illumination, and resolution on face recognition but also facilitates different experimental configurations with both 2D and 3D data. We conduct a series of experiments with various face recognition algorithms and point out how far they are from solving the face recognition problem under pose, illumination, and resolution variation. The dataset is publicly available and free for research use1.
AB - Face datasets are a fundamental tool to analyze the performance of face recognition algorithms. However, the accuracy achieved on current benchmark datasets is saturated. Although multiple face datasets have been published recently, they only focus on the number of samples and lack diversity on facial appearance factors, such as pose and illumination. In addition, while 3D data have been demonstrated improved face recognition accuracy by a significant margin, only a few 3D face datasets provide high quality 2D and 3D data. In this paper, we introduce a new and challenging dataset, called UHDB31, which not only allows direct measurement of the influence of pose, illumination, and resolution on face recognition but also facilitates different experimental configurations with both 2D and 3D data. We conduct a series of experiments with various face recognition algorithms and point out how far they are from solving the face recognition problem under pose, illumination, and resolution variation. The dataset is publicly available and free for research use1.
UR - http://www.scopus.com/inward/record.url?scp=85045561999&partnerID=8YFLogxK
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U2 - 10.1109/ICCVW.2017.300
DO - 10.1109/ICCVW.2017.300
M3 - Conference contribution
AN - SCOPUS:85045561999
T3 - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
SP - 2555
EP - 2563
BT - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 October 2017 through 29 October 2017
ER -