Personal identification using the rank level fusion of finger-knuckle-prints

Abstract

This paper presents the finger-knuckle-print (FKP) recognition system which comprises three functional phases namely: (1) novel technique for the feature extraction based on the structure function, (2) new classifier based on Triangular norms (T-norms), (3) novel techniques for the rank level fusion. The features derived from the structure function capture the variation in the texture of FKP. We have also proposed a classifier based on Frank T-norm which addresses the uncertainty in the intensity levels of image. We have also adapted the Choquet integral for the rank level fusion to improve further the identification accuracy of the individual FKP. The Choquet integral has never been used for the rank level fusion in the literature. The fuzzy densities will be learned using the reinforced hybrid bacterial foraging-particle swarm optimization (BF-PSO). The integral takes care of the overlapping information between the different instances of FKPs. We have also proposed the use of entropy based function for the rank level fusion. The rigorous experimental results of the rank level fusion show the significant improvement in the identification accuracy.