  The fingerprint classification is an important and effective method to
quicken the process and improve the accuracy in the fingerprint matching
process. Conventional supervised methods need a large amount of pre-labeled
data and thus consume immense human resources. In this paper, we propose a new
and efficient unsupervised deep learning method that can extract fingerprint
features and classify fingerprint patterns automatically. In this approach, a
new model named constraint convolutional auto-encoder (CCAE) is used to extract
fingerprint features and a hybrid clustering strategy is applied to obtain the
final clusters. A set of experiments in the NIST-DB4 dataset shows that the
proposed unsupervised method exhibits the efficient performance on fingerprint
classification. For example, the CCAE achieves an accuracy of 97.3% on only
1000 unlabeled fingerprints in the NIST-DB4.
