Using synthetic fingerprint images to test the performance of an AFIS system
| Type of publication: | Thesis |
| Citation: | Costa_THESIS_2022 |
| Year: | 2022 |
| Month: | July |
| School: | Université de Lausanne |
| Abstract: | An automatic biometric recognition system needs large-scale datasets to be trained and benchmarked which involves certain limitations in terms of time, money and privacy. Recent developments in the field of Artificial Intelligence (AI) and, more in details, the successes achieved by the Generative Adversarial Networks (Goodfellow et al., 2014) in the generation of synthetic images offer numerous possibilities trying to solve these constraints. In this study, the CFG fully synthetic fingerprint database of Bahmani et al. (2021) has been the subject of the experimentations to test the following hypothesis: (1) the results derived from the evaluation on generated synthetic fingerprints datasets are similar to a real one; (2) the intra- and inter-class variability of a real and a synthetic database are similar. Moreover, further works will develop more in detail the hypothesis (3), according to which, a fully synthetic fingerprint database could be used to train a biometric system (AFIS) instead of using a real fingerprint database. |
| Keywords: | Artificial intelligence, Automated Fingerprint Identification System (AFIS), Biometrics, Fingerprints, Generative Adversarial Networks (GAN) |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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