Hyperspectral face databases for facial recognition research

Abstract

Spectral imaging (SI) enables us to collect various spectral information at specific wavelengths by dividing the spectrum into multiple bands. As such, SI offers a means to overcome several major challenges specific to current face recognition systems. However, the practical usage of hyperspectral face recognition (HFR) has, to date, been limited due to database restrictions in the public domain for comparatively evaluating HFR. In this chapter, we review four publically available hyperspectral face databases (HFDs): CMU, PolyU-HSFD, IRIS-M, and Stanford databases toward providing information on the key points of each of the considered databases. In addition, a new large HFD, called IRIS-HFD-2014, is introduced. IRIS-HFD-2014 can serve as a benchmark for statistically evaluating the performance of current and future HFR algorithms and will be made publicly available.