Synthetic fingerprint generation (SFinGe) techniques and associated tools (e.g., SFinGe) were introduced more than 15 years ago [1]. The main aim was to generate large databases for performance evaluation without allocating huge amount of resources for acquisition campaigns and, at the same time, to conform with the privacy directives that in many countries limit the exchange of biometric data. While the original scope remains central today, since the generation of very large synthetic dataset is crucial to predict accuracy on very large scenarios, new security needs (such as detecting altered fingerprints) and algorithms improvements (supervised learning approaches) are continuously renewing interest in the generation of synthetic fingerprints.

Chapter Contents:

• 8.1 Introduction
• 8.2 The SFinGe approach
• 8.3 Tuning SFinGe to mimic natural feature distributions
• 8.4 Predicting performance in large-scale scenarios
• 8.5 Generating altered fingerprints
• 8.5.1 Categories of fingerprint alterations
• 8.5.2 Synthetic altered fingerprint generation
• 8.6 Using synthetic data to improve recognition algorithms
• 8.6.1 Generation of minutiae ground-truth data
• 8.6.2 Optimization of comparison algorithm for altered fingerprints
• 8.7 Conclusions
• References

Inspec keywords: fingerprint identification; learning (artificial intelligence)

Other keywords: biometric data; synthetic fingerprint generation; privacy directives; supervised learning approaches; synthetic dataset generation; performance evaluation; SFinGe techniques; acquisition campaigns; resource allocation

Subjects: Knowledge engineering techniques; Image recognition; Computer vision and image processing techniques