# Latent fingerprint minutia extraction using fully convolutional network

arXiv: https://arxiv.org/abs/1609.09850 · HTML: https://mlsanity.com/paper/1609.09850/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2016.

## Built on by (most cited first, 6 of 6)

- [Latent Fingerprint Recognition: Fusion of Local and Global Embeddings](https://mlsanity.com/paper/2304.13800/) (2023)
- [A Latent Fingerprint in the Wild Database](https://mlsanity.com/paper/2304.00979/) (2023)
- [Latent Fingerprint Matching via Dense Minutia Descriptor](https://mlsanity.com/paper/2405.01199/) (2024)
- [LEADER: Lightweight End-to-End Attention-Gated Dual Autoencoder for Robust Minutiae Extraction](https://mlsanity.com/paper/2602.15493/) (2026)
- [Latent fingerprint enhancement for accurate minutiae detection](https://mlsanity.com/paper/2409.11802/) (2024)
- [LatentPrintFormer: A Hybrid CNN-Transformer with Spatial Attention for Latent Fingerprint identification](https://mlsanity.com/paper/2511.08119/) (2025)

## Abstract (arXiv)

Minutiae play a major role in fingerprint identification. Extracting reliable minutiae is difficult for latent fingerprints which are usually of poor quality. As the limitation of traditional handcrafted features, a fully convolutional network (FCN) is utilized to learn features directly from data to overcome complex background noises. Raw fingerprints are mapped to a correspondingly-sized minutia-score map with a fixed stride. And thus a large number of minutiae will be extracted through a given threshold. Then small regions centering at these minutia points are entered into a convolutional neural network (CNN) to reclassify these minutiae and calculate their orientations. The CNN shares convolutional layers with the fully convolutional network to speed up. 0.45 second is used on average to detect one fingerprint on a GPU. On the NIST SD27 database, we achieve 53% recall rate and 53% precise rate that outperform many other algorithms. Our trained model is also visualized to show that we have successfully extracted features preserving ridge information of a latent fingerprint.