Development of spectral signatures and classification using hyperspectral face recognition

Abstract

Hyperspectral facial dataset represent innovative statistics, as compared to the traditional images and control the statistics in the subbands of Electromagnetic Spectrum (EMS) over a continuous range and produce the spectral libraries of all facial images. The research is accomplished on Carnegie Mellon University (CMU) Hyperspectral Face Datasets (HFDS) within the spectral series of 610nm to 1100 nm (VIR-NIR), having 50 spectral bands using ENVI 4.8 Software. Spectral Libraries are developed for different face attributes to identify faces for a period of time, even in the occurrence of changes in facial appearance. Supervised classification is performed by using classification techniques Spectral Angle Mapper (SAM). Experiments are accompanied to show the simplicity of the algorithm to classify and to develop spectral curves for hyperspectral face images.