LAUSR

A major limitation to advances in fingerprint presentation attack detection (PAD) is the lack of publicly available, large-scale datasets, a problem which has been compounded by increased concerns surrounding privacy and security of biometric data. Furthermore, most state-of-the-art PAD algorithms rely on deep networks which perform best in the presence of a large amount of training data. This work aims to demonstrate the utility of synthetic (both bona fide and PA style) fingerprints in supplying these algorithms with sufficient data to improve the performance of fingerprint PAD algorithms beyond the capabilities when training on a limited amount of publicly available “real” datasets. First, we provide details of our approach in modifying a state-of-the-art generative architecture to synthesize high quality bona fide and PA fingerprints. Then, we provide quantitative and qualitative analysis to verify the quality of our synthetic fingerprints in mimicking the distribution of real data samples. We showcase the utility of our synthetic bona fide and PA fingerprints in training a deep network for fingerprint PAD, which dramatically boosts the performance across three different evaluation datasets compared to an identical model trained on real data alone. Finally, we demonstrate that only 25% of the original (real) dataset is required to obtain similar detection performance when augmenting the training dataset with synthetic data. We make our synthetic dataset and model publicly available to encourage further research on this topic: https://github.com/groszste/SpoofGAN.