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The fingerprint classification is an important and effective method toquicken the process and improve the accuracy in the fingerprint matchingprocess. Conventional supervised methods need a large amount of pre-labeleddata and thus consume immense human resources. In this paper, we propose a newand efficient unsupervised deep learning method that can extract fingerprintfeatures and classify fingerprint patterns automatically. In this approach, anew model named constraint convolutional auto-encoder (CCAE) is used to extractfingerprint features and a hybrid clustering strategy is applied to obtain thefinal clusters. A set of experiments in the NIST-DB4 dataset shows that theproposed unsupervised method exhibits the efficient performance on fingerprintclassification. For example, the CCAE achieves an accuracy of 97.3% on only1000 unlabeled fingerprints in the NIST-DB4.

An Unsupervised Deep-Learning Method for Fingerprint Classification: the CCAE Network and the Hybrid Clustering Strategy