Multispectral Palmprint Recognition Using Score-Level Fusion

With the increasing demand of highly accurate and robust authentication system, palm print has widely been used in personal identification. Multispectral palm print has been proposed to get more distinguishing feature information and improve resistance to spoof. The vivid texture information of palm print presenting at different resolutions offers abundant prospects in personal recognition. So this paper describes a method to authenticate individuals based on multi-scale and multi-resolution palm print recognition. First, we extract palm print feature based on NSCT (nonsubsampled Contour transform), and store the feature using a hash table. Then the scores generated from each set of palm print image under red, green, blue and NIR spectrum, are combined using score level fusion method. This method use SUM and MAX operators. Comparatively low values of equal error rate and high recognition rate have been obtained for all fusion techniques. Multispectral palm print verification results on the ROI image libraries of PolyU demonstrate the effectiveness and accuracy of the proposed method.