Investigating Face Recognition from Hyperspectral Data: Impact of Band Extraction
Among various biometrics measures used in human identification, face recognition, has the distinct advantage of not requiring the subjects collaboration. Hyperspectral data constitute a natural choice for expanding face recognition image fusion, especially since it may provide information beyond the normal visible range, thus exceeding the normal human sensing. In this paper we investigate algorithms that improve face recognition by extracting the 'best bands' according to various criteria such as decorrelation and statistical independence. The work expands on previous band extraction results and has the distinct advantage of being one of the first that combines spatial information (i.e. face characteristics) with spectral information.
MSU Digital Commons Citation
Robila, Stefan; LaChance, Andrew; and Ruff, Shawna, "Investigating Face Recognition from Hyperspectral Data: Impact of Band Extraction" (2009). Department of Computer Science Faculty Scholarship and Creative Works. 353.