Personal Identification Using Multibiometrics Rank-Level Fusion

This paper investigates a new approach for the personal recognition using rank-level combination of multiple biometrics representations. There has been very little effort to study rank-level fusion approaches for multibiometrics combination and none using multiple palmprint representations. In this paper, we propose a new nonlinear rank-level fusion approach and present a comparative study of rank-level fusion approaches, which can be useful in combining multibiometrics fusion. The comparative experimental results from the publicly available multibiometrics scores and real hand biometrics data to evaluate/ascertain the rank-level combination using Borda count, logistic regression/weighted Borda count, highest rank method, and Bucklin method are presented. Our experimental results presented in this paper suggest that significant performance improvement in the recognition accuracy can be achieved as compared to those from individual palmprint representations. The rigorous experimental results presented in this paper also suggest that the proposed nonlinear rank-level approach outperforms the rank-level combination approaches presented in this paper.