Selection of hyperspectral bands by adopting a dimension reduction strategy for recognition of multispectral palmprint

Abstract

Palmprint is a unique and reliable biometric characteristic with high usability. Many works have been carried out on this field, during the past decades. Different algorithms and systems have been proposed and built successfully. Multispectral or hyperspectral palmprint imaging and recognition can be a potential solution to these systems because it can acquire more discriminative information for personal identity recognition. The Selection of the spectral bands is the most important step to develop the multispectral palmprint system. Most of the work done is based on methods by choosing the selected bands empirically. This work represents a preliminary study on the selection of bands by analyzing hyperspectral palmprint data (900nm ∼ 1600nm). We use an hyperspectral data provided by the “GPDShandsSWIRhyperspectral”. We adopted a dimension reduction strategy for the recognition of multispectral palmprint. We conducted a comparative study between two methods using the algorithms SOBI and JADE for the reduction of size bands. The results obtained showed that we can reduce the 20 bands chosen to 16 bands without having to modify the information from the image.