Protocol Based Similarity Evaluation of Publicly Available Synthetic and Real Fingerprint Datasets
2023 IEEE INTERNATIONAL JOINT CONFERENCE ON BIOMETRICS, IJCB（2023）
摘要
Several attempts have been made recently to generate synthetic fingerprint data. This has become necessary after legal changes in Europe and some US states in order to allow and continue long-term developments in the field of fingerprint biometrics. Apart from utilizing traditional methods (often based on Gabor filters), deep convolutional neural networks are widely used to generate synthetic fingerprint samples. The current study aims at comparing several publicly available synthetic fingerprint datasets with several datasets that consist of imprints taken from real people. To enable a comparison, first a detailed description of these datasets is carried out. Secondly, an available 4-level protocol is used, which is supposed to show similarities and/or differences between real and synthetic fingerprint samples in terms of quality assessment and non-mated as well as mated comparison scores' behavior. Furthermore, a new synthetic FP dataset composed of 50k samples is created and made publicly available in the course of this study.
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关键词
Fingerprint Dataset,Real Fingerprints,Deep Neural Network,Deep Convolutional Neural Network,Real Samples,Real People,Gabor Filters,As Mate,Fingerprints Of Samples,Change In Europe,Training Data,High Scores,Distribution Of Scores,Contributions Of This Work,Generative Adversarial Networks,Kernel Density,Recognition Performance,General Data Protection Regulation,Evaluation Protocol,Evaluation Methodology,Real Data Distribution,Synthetic Generation,Wasserstein Generative Adversarial Networks,Biometric Data,StyleGAN,Real Ones,Equal Error Rate,Crossmatch,Synthetic Ones
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