# Filter Design and Performance Evaluation for Fingerprint Image Segmentation

arXiv: https://arxiv.org/abs/1501.02113 · HTML: https://mlsanity.com/paper/1501.02113/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2015.

## Built on by (most cited first, 5 of 5)

- [Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints](https://mlsanity.com/paper/2107.01248/) (2021)
- [Sensor-invariant Fingerprint ROI Segmentation Using Recurrent Adversarial Learning](https://mlsanity.com/paper/2107.01361/) (2021)
- [LEADER: Lightweight End-to-End Attention-Gated Dual Autoencoder for Robust Minutiae Extraction](https://mlsanity.com/paper/2602.15493/) (2026)
- [Unleashing the Power of Simplicity: A Minimalist Strategy for State-of-the-Art Fingerprint Enhancement](https://mlsanity.com/paper/2603.19004/) (2026)
- [Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image Denoising](https://mlsanity.com/paper/2208.12810/) (2022)

## Abstract (arXiv)

Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: 'true' foreground can be labeled as background and features like minutiae can be lost, or conversely 'true' background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available.