A Survey on Synthetic Biometrics: Fingerprint, Face, Iris and Vascular Patterns

Synthetic biometric samples are created with an ultimate goal of getting around privacy concerns, mitigating biases in biometric datasets, and reducing the sample acquisition effort to enable large-scale evaluations. The recent breakthrough in the development of neural generative models shifted the focus from image synthesis by mathematical modeling of biometric modalities to data-driven image generation. This paradigm shift on the one hand greatly improves the realism of synthetic biometric samples and therefore enables new use cases, but on the other hand new challenges and concerns arise. Despite their realism, synthetic samples have to be checked for appropriateness for the tasks they are intended which includes new quality metrics. Focusing on sample images of fingerprint, face, iris and vascular patterns, we highlight the benefits of using synthetic samples, review the use cases, and summarize and categorize the most prominent studies on synthetic biometrics aiming at showing recent progress and the direction of future research

Visibility Assessment of Latent Fingerprints on Challenging Substrates in Spectroscopic Scans

Part 3: Extended AbstractsInternational audienceOur objectives for crime scene forensics are to find the substrates on which finger traces are visible in limited ranges of the electromagnetic spectrum using UV-VIS reflection spectroscopy and to determine the optimal ranges in the interval from 163 to 844 nm. We subjectively assess the visibility of fingerprints within detailed scans with a resolution of 500 ppi and compare the results with those of an automatic visibility assessment based on the streakiness score. Ten different substrates are evaluated, each with three fingerprints from different donors. Streakiness score is confirmed to be a suitable fingerprint visibility indicator on non-structured substrates. We identify two substrates, namely metallic paint and blued metal, on which ridge lines become visible exclusively in UV range from 200 to 400 nm and from 210 to 300 nm correspondingly

Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms

The datasets of synthetic biometric samples are created having in mind two major objectives: bypassing privacy concerns and compensating for missing sample variability in datasets of real biometric samples. If the purpose of generating samples is the evaluation of biometric systems, the foremost challenge is to generate so-called mated impressions—different fingerprints of the same finger. Note that for fingerprints, the finger’s identity is given by the co-location of minutiae points. The other challenge is to ensure the realism of generated samples. We solve both challenges by reconstructing fingerprints from pseudo-random minutiae making use of the pix2pix network. For controlling the identity of mated impressions, we derive the locations and orientations of minutiae from randomly created non-realistic synthetic fingerprints and slightly modify them in an identity-preserving way. Our previously trained pix2pix models reconstruct fingerprint images from minutiae maps, ensuring that the realistic appearance is transferred from training to synthetic samples. The main contribution of this work lies in creating and making public two synthetic fingerprint datasets of 500 virtual subjects with 8 fingers each and 10 impressions per finger, totaling 40,000 samples in each dataset. Our synthetic datasets are designed to possess characteristics of real biometric datasets. Thus, we believe they can be applied for the privacy-friendly testing of fingerprint recognition systems. In our evaluation, we use NFIQ2 for approving the visual quality and Verifinger SDK for measuring the reconstruction success

Computer-Aided Contact-Less Localization of Latent Fingerprints in Low-Resolution CWL Scans

Part 2: Work in ProgressInternational audienceIn forensic investigations, the recovering of latent fingerprints is one of the most essential issues. Driven by human experts, today this process is very time consuming. An automation of both examination of suspicious areas and acquisition of fingerprints lead on the one hand to the covering of larger surfaces and on the other hand to significant speed up of the evidence collection. This work presents an experimental study on capabilities of chromatic white-light sensor (CWL) regarding the contact-less localization of latent fingerprints on differently challenging substrates. The fully automatic CWL-based system is implemented from the acquisition through the feature extraction right up to the classification. The key objective of the work is to develop a methodological approach for the quantitative evaluation of the localization success. Based on the proposed performance measures, the optimal system parameters such as scan resolution, extracted features and classification scheme are specified dependent on the surface material. Our experiments from an actual project with the sensor industry partner show convincing localization performance on easy-to-localize and adequate performance on moderate-to-localize substrates. The hard-to-localize substrates require further improvements of the localization system