Recently, there is a growing interest to employ biometrics in post-mortem forensics, mainly to replace cost intensive radiology based imaging devices. While it has been shown that post-mortem biometric recognition is feasible for fingerprints, face and iris, no studies regarding post-mortem vasculature pattern recognition have been published. Based on the first reported post-mortem hand- and finger-vein dataset, the hypothesis, that hand vasculature biometrics can be used as post-mortem biometric modality, is falsified. Using an indirect proof, it is shown that no usable vascular features are present in the small amount of sample data collected, by visual inspection as well as by applying several biometric quality metrics, which confirm that hand-based vasculature biometrics can not be used as post-mortem biometric modality.

Generation of synthetic biometric samples such as, for instance, fingerprint images gains more and more importance especially in view of recent cross-border regulations on security of private data. The reason is that biometric data is designated in recent regulations such as the EU GDPR as a special category of private data, making sharing datasets of biometric samples hardly possible even for research purposes. The usage of fingerprint images in forensic research faces the same challenge. The replacement of real datasets by synthetic datasets is the most advantageous straightforward solution which bears, however, the risk of generating "unrealistic" samples or "unrealistic distributions" of samples which may visually appear realistic. Despite numerous efforts to generate high-quality fingerprints, there is still no common agreement on how to define "high-quality'' and how to validate that generated samples are realistic enough. Here, we propose general requirements on synthetic biometric samples (that are also applicable for fingerprint images used in forensic application scenarios) together with formal metrics to validate whether the requirements are fulfilled. Validation of our proposed requirements enables establishing the quality of a generative model (informed evaluation) or even the quality of a dataset of generated samples (blind evaluation). Moreover, we demonstrate in an example how our proposed evaluation concept can be applied to a comparison of real and synthetic datasets aiming at revealing if the synthetic samples exhibit significantly different properties as compared to real ones.

In this work, we investigate the possibility of generating a grayscale image of the finger vein from its binary template. This exercise would allow us to determine the invertibility of finger vein templates, and this has implications in biometric security and privacy. While such an analysis has been undertaken in the context of face, fingerprint and iris templates, this is the first work involving the finger vein biometric trait. The transformation from binary features to a grayscale image is accomplished using a Pix2Pix Convolutional Neural Network (CNN). The reversibility of 6 different types of binary features is evaluated using this CNN. Further, a number of experiments are conducted using 7 distinct finger vein datasets. Results indicate that (a) it is possible to reconstruct finger vein images from their binary templates; (b) the reconstructed images can be used for biometric recognition purposes; (c) the CNN trained on one dataset can be successfully used for reconstructing images in a different dataset (cross-dataset reconstruction); and (d) the images reconstructed from one set of features can be successfully used to extract a different set of features for biometric recognition (cross-feature-set generalization).

Being aware of the origin (source sensor) of an iris images offers several advantages. Identifying the specific sensor unit supports ensuring the integrity and authenticity of iris images and thus detecting insertion attacks at a biometric system. Moreover, by knowing the sensor model selective processing, such as image enhancements, becomes feasible. In order to determine the origin (i.e. dataset) of near-infrared (NIR) and visible spectrum iris/ocular images, we evaluate the performance of three different approaches, a photo response non-uniformity (PRNU) based and an image texture feature based one, and the fusion of both. Our first set of experiments includes 19 different datasets comprising different sensors and image resolutions. The second set includes 6 different camera models with 5 instances each. We evaluate the applicability of the three approaches in these test scenarios from a forensic and non-forensic perspective.

In this paper we present a metric for visual security evaluation of encrypted images, also known as visual security metric. Such a metric should be able to assess whether an image encryption method is secure or not. In order to consider intelligibility of objects in encrypted images our metric is based on image segmentation and applying a measure designed to evaluate the segmentation result. The visual security metrics' performance is evaluated using a selective encryption approach and compared to some general image quality metrics like PSNR, metrics suggested for encrypted images like Irregular Deviation and two metrics specifically designed for visual security evaluation. Our visual security metric performs better than all of the other tested metrics on the dataset and encryption algorithm we used during our experiments in terms of different correlation measures.