Summary of Latent Fingerprint Enhancement For Accurate Minutiae Detection, by Abdul Wahab et al.
Latent fingerprint enhancement for accurate minutiae detection
by Abdul Wahab, Tariq Mahmood Khan, Shahzaib Iqbal, Bandar AlShammari, Bandar Alhaqbani, Imran Razzak
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of identifying suspects based on partial and smudged fingerprints, also known as fingermarks or latent fingerprints. Current methods for matching latent fingerprints rely heavily on local minutiae-based embeddings, neglecting global representations that are crucial for accurate fingerprint recognition. To address this, the authors propose a novel approach using generative adversary networks (GANs) to redefine Latent Fingerprint Enhancement (LFE). The model produces enhanced latent fingerprints with exceptional fidelity to ground-truth instances, leading to significant improvements in identification performance. The framework integrates minutiae locations and orientation fields, preserving both local and structural fingerprint features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to improve the process of identifying people based on their fingerprints when they are not very clear or complete. Right now, most methods for matching these kinds of fingerprints rely too much on small details, like ridges and valleys, and don’t take into account bigger patterns. The researchers in this study use something called generative adversary networks (GANs) to make the fingerprints clearer and more accurate. This helps the process of identifying people work better. The authors tested their method on two big datasets and showed that it works much better than other methods. |
