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Face recognition is not a new area of study, but facial recognition using through hyperspectral images is a somewhat new concept which is still in its infancy. Although the conventional method of face recognition using Red-Green-Blue (RGB) or grayscale images has been advanced over the last twenty years, these methods are still shown to have weak performance whenever there are variations or changes in lighting, pose, or temporal aspect of the subjects. A hyperspectral representation of an image captures more information that is available within a scene than a RGB image therefore it is beneficial to study the performance of face recognition using a hyperspectral representation of the subjects' faces. We studied the results of a variety of methods for performing face recognition using the Scale Invariant Transformation Feature (SIFT) algorithm as a matching function on uncorrelated spectral bands, principal component representation of the spectral bands, and the ensemble decision of the two. We conclude that there is no dominating method in the scope of our research; however, we do obtain three methods with leading performances despite some trade-off between performance at lower ranks and performance at higher ranksthat outperform the results obtained from a previous study which only considered a SIFT application on a single hyperspectral band which also performs very well under temporal variation.
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Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.