Dynamic registration selection for fingerprint verification

Information fusion is a powerful approach to increasing the accuracy of biometric authentication systems, and is currently an active area of research. The majority of studies focus on combining the results from multiple verification systems at the match score level using either a classification or combination scheme. However, there are advantages to performing the fusion at an earlier stage of processing. Fingerprint registration involves finding the translation and rotation parameters that align two fingerprints; a challenging problem that can be approached in a number of ways. The fusion of fingerprint alignment algorithms is introduced in the form of dynamic registration selection. A Bayesian statistical framework is used to select the most probable alignment produced by competing algorithms. The results of the proposed technique are tested on multiple FVC 2002 databases, and are shown to outperform methods based on match score combination.