Fingerprint recognition using model-based density map

Utilizing more information other than minutiae is much helpful for large-scale fingerprint recognition applications. In this paper, we proposed a polynomial model to approximate the density map of fingerprints and used the model's parameters as a novel kind of feature for fingerprint representation. Thus, the density information can be utilized into the matching stage with a low additional storage cost. A decision-level fusion scheme is further used to combine the density map matching with conventional minutiae-based matching and experimental results showed a much better performance than using single minutiae-based matching.

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