Fingerprint recognition using model-based density map.

Dingrui Wan; Jie Zhou

doi:10.1109/tip.2006.873442

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Fingerprint recognition using model-based density map.

Dingrui Wan·Jie Zhou

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Abstract

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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Algorithms Artificial Intelligence Computer Simulation Dermatoglyphics Fingers Humans Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Models, Biological Models, Statistical Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Skin Statistical Distributions Comparative Study Journal Article Research Support, Non-U.S. Gov't

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Dingrui Wan, Jie Zhou. 2006. Fingerprint recognition using model-based density map.. https://doi.org/10.1109/tip.2006.873442

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