QUEST Hierarchy for Hyperspectral Face Recognition

[摘要] A qualia exploitation of sensor technology (QUEST) motivated architecture using algorithm fusion and adaptive feedback loops for face recognition for hyperspectral imagery (HSI) is presented. QUEST seeks to develop a general purpose computational intelligence system that captures the beneficial engineering aspects of qualia-based solutions. Qualia-based approaches are constructed from subjective representations and have the ability to detect, distinguish, and characterize entities in the environmentAdaptive feedback loops are implemented that enhance performance by reducing candidate subjects in the gallery and by injecting additional probe images during the matching process. The architecture presented provides a frameworkfor exploring more advanced integration strategies beyond those presented. Algorithmic results and performance improvements are presented as spatial, spectral, and temporal effects are utilized; additionally, a Matlab-based graphical user interface (GUI) is developed to aid processing, track performance, and to display results.

[学科分类] 人工智能