Fingerprint identification has been regarded as the most reliable biometric identification method, which has been widely used in the fields of criminal investigation, resident identification, and immigration personnel identification. The feature of these applications is that one query fingerprint needs to be quickly and accurately compared with all the fingerprints in the mass fingerprint database to identify the owner of the fingerprint. In recent years, the size of the fingerprint database was expanded rapidly with the law of fingerprint collection coming into effect. For one thing the image difference bewteen fingerprints in the same type increases significantly, for another the similarity of different types of fingerprint images also increases, which makes the misclassification rate of fingerprint classification algorithm greatly increase. At the same time, "massive fingerprint precision classification" has become a hot spot in the field of public security application and fingerprint identification. Aiming at solving the above problems, we propose an accurate fingerprint classification mode Caps-FingerNet based on capsule networks. On the one hand, the mode combines the unique network characteristics of the capsule networks with the individual self-similar texture features of fingerprint images to form a more robust feature extractor and classifier. Meanwhile, the batch-normalization method avoids the shortcoming of gradient disappearance in typical capsule networks. On the other hand, the attention capsule mechanism is innovatively introduced in this study, making Caps-FingerNet more accurate and comprehensive than typical capsule networks in extracting fingerprint image details, and the global squashing algorithm is used to squash the capsule effectively. Cap-FingerNet achieves an extreme accuracy of 99.63% in the four-class fingerprint classification of a provincial public security criminal fingerprint database. The accuracy rates of 96.25% and 94.5% were measured on the four and five classification tasks of NIST-DB04 fingerprint dataset, respectively.
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