An approach to SWIR hyperspectral hand biometrics

Three feature extraction methods for hyperspectral hand curve characterization are examined. They are based on the area, slope or curvature at different automatically selected spatial hand positions. We report a set of experiments which explore: best hand zones for extracting local hyperspectral features; robustness against the number of training samples; error detection; and occlusion. Different strategies for combining the spectral features with geometric traits available in the hyperspectral cube are discussed. Our experiments show that local spectral properties of human tissue are effective discriminants for biometric recognition with a performance near to or better than that obtained by other hand traits. Equal Error Rates of 0.05% and an identification rate of 96.71% are obtained from a database of 154 people. These results along with their higher robustness to spoofing attacks make the hyperspectral features a promising alternative for person identification.