Advanced Image Quality Assessment for Hand- and Fingervein Biometrics

Natural Scene Statistics commonly used in non-reference image quality measures and a deep learning based quality assessment approach are proposed as biometric quality indicators for vasculature images. While NIQE and BRISQUE if trained on common images with usual distortions do not work well for assessing vasculature pattern samples' quality, their variants being trained on high and low quality vasculature sample data behave as expected from a biometric quality estimator in most cases (deviations from the overall trend occur for certain datasets or feature extraction methods). The proposed deep learning based quality metric is capable of assigning the correct quality class to the vaculature pattern samples in most cases, independent of finger or hand vein patterns being assessed. The experiments were conducted on a total of 13 publicly available finger and hand vein datasets and involve three distinct template representations (two of them especially designed for vascular biometrics). The proposed (trained) quality measures are compared to a several classical quality metrics, with their achieved results underlining their promising behaviour.

Enabling Fingerprint Presentation Attacks: Fake Fingerprint Fabrication Techniques and Recognition Performance

Fake fingerprint representation pose a severe threat for fingerprint based authentication systems. Despite advances in presentation attack detection technologies, which are often integrated directly into the fingerprint scanner devices, many fingerprint scanners are still susceptible to presentation attacks using physical fake fingerprint representation. In this work we evaluate five different commercial-off-the-shelf fingerprint scanners based on different sensing technologies, including optical, optical multispectral, passive capacitive, active capacitive and thermal regarding their susceptibility to presentation attacks using fake fingerprint representations. Several different materials to create the fake representation are tested and evaluated, including wax, cast, latex, silicone, different types of glue, window colours, modelling clay, etc. The quantitative evaluation includes assessing the fingerprint quality of the samples captured from the fake representations as well as comparison experiments where the achieved matching scores of the fake representations against the corresponding real fingerprints indicate the effectiveness of the fake representations. Our results confirmed that all except one of the tested devices are susceptible to at least one type/material of fake fingerprint representations.