Abstract
A major approach for fingerprint matching today is based on minutiae. However, due to the lack of minutiae, their accuracy degrades significantly for partial-to-partial matching. We propose a novel matching algorithm that makes full use of the distinguishing information in partial fingerprint images. Our model employs the Phase-Only Correlation (POC) function to coarsely assign two fingerprints. Then we use a deep convolutional neural network (CNN) with spatial pyramid pooling to measure the similarity of the overlap areas. Experiments indicate that our algorithm has an excellent performance.
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Acknowledgments
This work was funded by the Chinese National Natural Science Foundation (11331012, 11571014, 11731013).
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Qin, J., Tang, S., Han, C., Guo, T. (2017). Partial Fingerprint Matching via Phase-Only Correlation and Deep Convolutional Neural Network. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_64
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DOI: https://doi.org/10.1007/978-3-319-70136-3_64
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