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

arXiv: https://arxiv.org/abs/2109.05526 · HTML: https://mlsanity.com/paper/2109.05526/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 64 references that are in the map.
- First posted on arXiv 12 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction](https://mlsanity.com/paper/1709.02228/) (2017)
- [Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations](https://mlsanity.com/paper/1802.00187/) (2018)
- [Fingerprint Feature Extraction by Combining Texture, Minutiae, and Frequency Spectrum Using Multi-Task CNN](https://mlsanity.com/paper/2008.11917/) (2020)
- [FingerNet: Pushing The Limits of Fingerprint Recognition Using Convolutional Neural Network](https://mlsanity.com/paper/1907.12956/) (2019)
- [On the use of convolutional neural networks for robust classification of multiple fingerprint captures](https://mlsanity.com/paper/1703.07270/) (2017)

## Built on by (most cited first, 2 of 2)

- [An Unsupervised Deep-Learning Method for Bone Age Assessment](https://mlsanity.com/paper/2206.05641/) (2022)
- [Simple but Effective Unsupervised Classification for Specified Domain Images: A Case Study on Fungi Images](https://mlsanity.com/paper/2311.08995/) (2023)

## Abstract (arXiv)

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.