Summary of A Large-scale Operational Study Of Fingerprint Quality and Demographics, by Javier Galbally et al.
A large-scale operational study of fingerprint quality and demographics
by Javier Galbally, Aleksandrs Cepilovs, Ramon Blanco-Gonzalo, Gillian Ormiston, Oscar Miguel-Hurtado, Istvan Sz. Racz
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper investigates the relationship between demographics (gender, age, finger-type) and fingerprint recognition performance using a large-scale database of 10-print impressions from almost 16,000 subjects. The results show that there is a degree of performance variability in fingerprint-based recognition systems for different demographic segments. The study provides data-driven evidence, plausible hypotheses to explain the findings, and suggests potential follow-up actions to reduce observed fingerprint quality differences. The paper’s contribution aims to increase algorithmic fairness and equality in biometric technology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well a fingerprint recognition system works for different groups of people (based on gender, age, and finger shape). They use a big database with over 16,000 fingerprints to see if certain groups have better or worse results. The study finds that the system doesn’t work equally well for everyone, but it provides some clues about why this might be happening. The goal is to make fingerprint recognition more fair and equal by understanding these differences. |
