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 environment Adaptive 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 framework for 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. 1. Introduction Social interaction depends heavily on the amazing face recognition capability that humans possess, especially the innate ability to process facial information. In a myriad of environments and views, people are able to quickly recognize and interpret visual cues from another person’s face. With an increasing focus on personal protection and identity verification in public environments and during common interactions (e.g., air travel, financial transactions, and building access), the performance capability of the human system is now a desired requirement of our security and surveillance systems. Face recognition is a crucial tool being used in current operations in Iraq and Afghanistan by allied forces to identify and track enemies [1] and effectively distinguish friendlies and nonenemies [2]. The human recognition process utilizes not only spatial information but also important spectral and temporal aspects as well. Utilizing only visual wavelengths for computer vision solutions has significant downsides, where features evident to humans are too subtle for a machine to capture. Prior research has shown deficiencies in computer vision techniques compared to human or animal vision when detecting defects in parts [4] or biometric identification [5]. By increasing the spectral sampling to include nonvisible wavelengths it might be possible to detect some of these subtle features included in the facial data. However, incorporation and handling of features in multispectral or hyperspectral imagery
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