Fingerprint growth model for mitigating the ageing effect on children’s fingerprints matching
| Autor: | Laurent Beslay, Rudolf Haraksim, Javier Galbally |
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| Rok vydání: | 2019 |
| Předmět: |
Minutiae
Matching (statistics) Biometrics Computer science business.industry Fingerprint (computing) Sample (statistics) Pattern recognition 02 engineering and technology Fingerprint recognition 01 natural sciences Displacement (vector) Artificial Intelligence Fingerprint 0103 physical sciences Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence 010306 general physics business Software |
| Zdroj: | Pattern Recognition. 88:614-628 |
| ISSN: | 0031-3203 |
| Popis: | Nowadays, the majority of fingerprint quality, matching and feature extraction algorithms are developed and trained on fingerprints of adults. Accordingly, the processing of children’s fingerprints presents performance issues derived for the most part from: (1) their smaller size and finer ridge structure; (2) their higher variability over time due to the displacement of minutiae induced by growth. The present article is focused on the second factor. The rapid growth of children fingerprints causes a significant displacement of the minutiae points between samples of the same finger acquired with a few years distance from each other. This displacement results in a decrease of the accuracy of fingerprint recognition systems when the reference and probe sample drift apart in time. This effect is known as biometric ageing. In the present study we propose to address this issue by developing and validating a minutiae-based growth model, derived from a database of over 60,000 children’s fingerprints, acquired in real operational conditions, ranging between 5 and 16 years of age, with a time difference between fingerprint pairs re-enrolments of up to 6 years. We analyze two potential application scenarios for the developed growth model. On one hand, we use the model to grow children’s fingerprints in order to spread out the minutiae points to attain sizes similar to those of a sample captured at a later point in time. On the other hand, we apply the model to rejuvenate fingerprints enrolled at a later stage by contracting the minutiae points so that their location is more similar to those of a sample acquired earlier. In both scenarios, the application of the growth model to produce artificially grown/rejuvenated fingerprint minutiae templates results in a significant improvement of the matching scores compared to the ones produced by original fingerprints. |
| Databáze: | OpenAIRE |
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