This paper investigates if the performance of hyperspectral face recognition algorithms can be improved by considering 1D projections of the whole spectral data along the spectral dimension. Three different projections are investigated: single spectral band selection, non-negative spectral band combination, and unbounded spectral band combination. Experiments are performed on a standard hyperspectral dataset and the obtained results outperform seven existing hyperspectral face recognition algorithms.
Bianco, S. (2015). Can linear data projection improve hyperspectral face recognition?. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.161-170). Springer Verlag [10.1007/978-3-319-15979-9_16].
Can linear data projection improve hyperspectral face recognition?
BIANCO, SIMONE
2015
Abstract
This paper investigates if the performance of hyperspectral face recognition algorithms can be improved by considering 1D projections of the whole spectral data along the spectral dimension. Three different projections are investigated: single spectral band selection, non-negative spectral band combination, and unbounded spectral band combination. Experiments are performed on a standard hyperspectral dataset and the obtained results outperform seven existing hyperspectral face recognition algorithms.
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