A Polarimetric Thermal Database for Face Recognition Research

Shuowen Hu, Nathaniel J. Short, Benjamin S. Riggan, Christopher Gordon, Kristan P. Gurton, Matthew Thielke, Prudhvi Gurram, Alex L. Chan; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 119-126

Abstract


We present a polarimetric thermal face database, the first of its kind, for face recognition research. This database was acquired using a polarimetric longwave infrared imager, specifically a division-of-time spinning achromatic retarder system. A corresponding set of visible spectrum imagery was also collected, to facilitate cross-spectrum (also referred to as heterogeneous) face recognition research. The database consists of imagery acquired at three distances under two experimental conditions: neutral/baseline condition, and expressions condition. Annotations (spatial coordinates of key fiducial points) are provided for all images. Cross-spectrum face recognition performance on the database is benchmarked using three techniques: partial least squares, deep perceptual mapping, and coupled neural networks.

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[bibtex]
@InProceedings{Hu_2016_CVPR_Workshops,
author = {Hu, Shuowen and Short, Nathaniel J. and Riggan, Benjamin S. and Gordon, Christopher and Gurton, Kristan P. and Thielke, Matthew and Gurram, Prudhvi and Chan, Alex L.},
title = {A Polarimetric Thermal Database for Face Recognition Research},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2016}
}