Recent developments enable biometric recognition systems to be available as mobile solutions or to be even integrated into modern smartphone devices. Thus, smartphone devices can be used as mobile fingerprint image acquisition devices, and it has become feasible to process fingerprints on these devices, which helps police authorities carry out identity verification. In this paper, we provide a comprehensive and in-depth engineering study on the different stages of the fingerprint recognition toolchain. The insights gained throughout this study serve as guidance for future work towards developing a contactless mobile fingerprint solution based on the iPhone 11, working without any additional hardware. The targeted solution will be capable of acquiring 4 fingers at once (except the thumb) in a contactless manner, automatically segmenting the fingertips, pre-processing them (including a specific enhancement), and thus enabling fingerprint comparison against contact-based datasets. For fingertip detection and segmentation, various traditional handcrafted feature-based approaches as well as deep-learning-based ones are investigated. Furthermore, a run-time analysis and first results on the biometric recognition performance are included.

Current fingerprint identification systems face significant challenges in achieving interoperability between contact‐based and contactless fingerprint sensors. In contrast to existing literature, we propose a novel approach that can combine pose correction with further enhancement operations. It uses deep learning models to steer the correction of the viewing angle, therefore enhancing the matching features of contactless fingerprints. The proposed approach was tested on real data of 78 participants (37,162 contactless fingerprints) acquired by national police officers using both contact‐based and contactless sensors. The study found that the effectiveness of pose correction and unwarping varied significantly based on the individual characteristics of each fingerprint. However, when the various extension methods were combined on a finger‐wise basis, an average decrease of 36.9% in equal error rates (EERs) was observed. Additionally, the combined impact of pose correction and bidirectional unwarping led to an average increase of 3.72% in NFIQ 2 scores across all fingers, coupled with a 6.4% decrease in EERs relative to the baseline. The addition of deep learning techniques presents a promising approach for achieving high‐quality fingerprint acquisition using contactless sensors, enhancing recognition accuracy in various domains.

In this work, we study the applicability of PRNU-based sensor identification methods for finger vein imagery. We also investigate the effect of different image regions on the identification performance by looking at five different crop-pings with different sizes. The proposed method is tested on eight publicly available finger vein datasets. For each finger vein sensor a noise reference pattern is generated and subsequently matched with noise residuals extracted from previously unseen finger vein images. Although the final result strongly encourages the use of PRNU-based approaches for sensor identification, it can also be observed that the choice of image region for PRNU extraction is crucial. The result clearly shows that regions containing biometric trait (varying content) should be preferred over background regions containing non-biometric trait (identical content).

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.