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

• URL: http://arxiv.org/abs/2109.05526v1
• Date: Sun, 12 Sep 2021 14:35:59 GMT
• Title: An Unsupervised Deep-Learning Method for Fingerprint Classification: the CCAE Network and the Hybrid Clustering Strategy
• Authors: Yue-Jie Hou, Zai-Xin Xie, Jian-Hu, Yao-Shen, and Chi-Chun Zhou
• Abstract summary: We propose a new and efficient unsupervised deep learning method that can extract fingerprint features and classify fingerprint patterns automatically. A set of experiments in the NIST-DB4 dataset shows that the proposed unsupervised method exhibits the efficient performance on fingerprint classification.
• Score: 2.370553892492642
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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