A Parallelized Algorithm for Hyperspectral Biometrics

Christopher Baker, Timothee Bouhour, Akshay Malik
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
University of Illinois, Final Report for ECE 445, 2012



   author={Baker, C. and Bouhour, T. and Malik, A.},



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The parallelized algorithm for hyperspectral biometrics uses the processing power of a GPU (Graphical Processing Unit) to compare hyperspectral images of people’s faces. The feature extraction algorithm first retrieves uniquely identifiable features from raw hyperspectral data from 64 bands and creates both a database and individual target files. Using these files, the comparison algorithm written in CUDA C compares a given target against the database and returns the top five matches, their calculated distance from the target, and their security clearance level. A wireless door locking mechanism can be engaged to simulate unlocking and re-locking any of four doors based on the given security rating. The feature extraction algorithm is accurate to within 2% of actual location and the comparison algorithm returns the target in the top 5 matches 65% of the time. The wireless door locking assembly works as expected although it occasionally has packet corruption errors in its communication. Improvements can also be made in the range of data that the feature extraction algorithm accepts and in the accuracy and speed of the comparison algorithm.
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