The Fingerprint Liveness Detection Competition (LivDet) is a recurring benchmark series that evaluates the effectiveness of software-based Presentation Attack Detection (PAD) algorithms in fingerprint recognition. LivDet2025 presents three challenges: (1) "Liveness Detection in Action", requiring the integration of PAD with user-specific recognition; (2) "Fingerprint Representation", evaluating the compactness and discriminability of feature vectors; and (3) "Adversarial Robustness", assessing the resilience of PADs to adversarially-crafted presentation attack instruments. This edition marks a significant milestone with the inclusion of contactless fingerprint data, promoting interoperability and robustness across acquisition technologies. Furthermore, no training data was provided; participants must select and declare external datasets for model development. The competition was open to academic and industrial research groups, with all submitted algorithms evaluated on common datasets and under standardized protocols. LivDet2025 aims to provide a comprehensive assessment of PAD performance under realistic, multi-sensor, and multi-attack scenarios. Results reveal important trade-offs between PAD accuracy, usability, and computational efficiency. For instance, some systems achieved high presentation attack rejection at the cost of extremely high false rejection rates, while others optimised speed and generalizability but exhibited limited attack resilience.

Contactless fingerprint recognition is an emerging biometric technology and Presentation Attack Detection (PAD) methods are crucial to preserve system security. Convolutional Neural Networks (CNNs) represent the state-of the-art of PAD algorithms for many contactless captured biometric characteristics and various research groups proposed specialized CNN-based PAD methods or used general purpose CNNs to detect Presentation Attacks (PAs). In this work, we compare nine CNN-based PAD methods for contactless fingerprint PAD: five general purpose algorithms, and four dedicated PAD methods designed for various biometric characteristics. To achieve this, we combine the COLFISPOOF database with three bona fide databases: the HDA database and both versions of the ISPFD database. We set up our experiments using a baseline evaluation protocol and four Leave-One-Out (LOO) protocols, to benchmark the generalization capabilities to unseen data. The results reported by using the Attack Presentation Classification Error Rate (APCER) vs. Bona fide Presentation Classification Error Rate (BPCER) and the Detection Equal Error Rate (D-EER). Further, we discuss the achieved results in detail and give recommendations for real-world implementations. Our results show that established PAD algorithms for other biometric characteristics can accurately detect PAs on contactless fingerprints. While strong deviations between the considered PAD algorithms are observed, the best performing method shows a D-EER between 0.01% and 0.08% (depending on the LOO partition) and a APCER of 0.00% at a BPCER of 1.00%.

Synthetic fingerprint image generation is important for the development of biometric recognition systems at scale due to the lack of easy-to-distribute real data, e.g. due to legal restrictions. Based on a SFinGe ridge line pat-tern, the recently introduced SynCoLFinGer algorithm gen-erates synthetic contactless fingerprints. However, it neglects some of the most frequent imperfections shown in common fingerprint databases. This work aims to analyse the most frequently found imperfections on fingerprint images and implements additional imperfections to SynCoLFinGer, including ink stains, dermatological issues, wounds and scars. The resem-blance of the generated fingerprints is assessed with re-gard to their sample quality and visually in comparison to real samples. Moreover, we report the biometric performance and sample quality on a generated database. The source code of this work is made publicly available under: https://gitlab.com/jannispriesnitz/syncolfinger

We present the first method for synthetic generation of contactless fingerprint images, referred to as SynCoLFinGer. To this end, the constituent components of contactless fingerprint images regarding capturing, subject characteristics, and environmental influences are modeled and applied to a synthetically generated ridge pattern using the SFinGe algorithm. The proposed method is able to generate different synthetic samples corresponding to a single finger and it can be parameterized to generate contactless fingerprint images of various quality levels. The resemblance of the synthetically generated contactless fingerprints to real fingerprints is confirmed by evaluating biometric sample quality using an adapted NFIQ 2.0 algorithm and biometric utility using a state-of-the-art contactless fingerprint recognition system.