A comparative study of combining multiple enrolled samples for fingerprint verification

In fingerprint verification systems, there are usually multiple (from two to four) enrolled impressions for a same finger. The performance of the systems can be improved by combining these impressions through feature fusion or decision fusion strategy. In this paper, different schemes to combine multiple enrolled impressions are comparatively studied. Experimental results show that a larger improvement can be obtained by using decision fusion scheme than feature fusion. In all decision fusion rules, sum rule outperforms voting rule a little whether using similarity or Neyman–Pearson rule. Based on the observation that the performance of these two strategies can complement each other, we also propose a novel fusion scheme to further combine feature fusion and decision fusion, which can produce an even better result.