Hyperspectral cameras provide additional information in terms of multiple sampling of the visible spectrum, holding information that could be potentially useful for biometric applications. This paper investigates whether the performance of hyperspectral face recognition algorithms can be improved by considering single and multiple one-dimensional (1-D) projections of the whole spectral data along the spectral dimension. Three different projections are investigated and found by optimization: single-spectral band selection, nonnegative spectral band combination, and unbounded spectral band combination. Since 1-D projections can be performed directly on the imaging device with color filters, projections are also restricted to be physically plausible. The experiments are performed on a standard hyperspectral dataset and the obtained results outperform eight existing hyperspectral face recognition algorithms.