Biometrics based on palmprint has been developing fast in the past ten years. The newly proposed hyperspectral imaging can provide high accuracy and abundant information about the palms and the tissues, veins underneath. However, due to the limitations of computation speed and storage, we have to select the most representative bands for palmprint recognition. This paper proposes a band selection scheme for hyperspectral palmprint recognition. First, the images with high image entropies and Equal Error Rate (EER) are selected. Then a clustering method is introduced to choose the most representable bands. In our experiments on the HK-PolyU Hyperspectral Palmprint Database, three bands combination can generate the best EER 0.17325%. The proposed approach can also be used for band selection of other hyperspectral systems.
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