This paper presents a summary of the 2nd Latent in the Wild Fingerprint Recognition Competition held at the 2025 International Joint Conference on Biometrics. The competition has two tracks: latent fingerprint 1) recognition, and 2) quality assessment. It attracted a total of 12 participating teams from academia and industry for both tracks, representing 10 countries. In total, 8 valid submissions were evaluated by the organizers. The competition aimed to advance the state-of-the-art in latent fingerprint recognition and quality assessment by providing a challenging dataset of latent fingerprints collected in natural, non-ideal conditions. This paper summarizes the dataset, evaluation protocols, submitted methods, and the competition results.

This paper presents a summary of the Latent in the Wild Fingerprint Recognition Competition held at the 2024 International Joint Conference on Biometrics (IJCB 2024). The competition attracted a total of 6 participating teams from academia and industry, representing 6 countries. In total, 3 valid submissions were evaluated by the organizers. The competition aimed to advance the state-of-the-art in latent fingerprint recognition by providing a challenging dataset of latent fingerprints collected in natural, non-ideal conditions. This paper summarizes the dataset, evaluation criteria, participant methods, and the competition results.

The quality assessment of fingermarks (latent fingerprints) is an essential part of a forensic investigation. It indicates how valuable the fingermarks are as forensic evidence, it determines how they should be further processed, and it correlates with the likelihood of successful identification, i.e., finding a matching fingerprint in a reference database. Since the environments in which fingermarks are found are not controlled, this task proves challenging even with modern machine learning solutions. In this work, we propose a predictive framework for automated fingermark quality assessment (AFQA). With this iteration of AFQA, we bridge the gap between the classic machine learning approach with handcrafted features and the modern deep learning paradigm, evaluate the advantages and disadvantages of these methodologies, and provide the rationale and direction for future development of AFQA methods. We present a significantly improved AFQA toolbox and provide a quality aggregation method capable of fusing together multiple predicted quality values from an ensemble of quality assessment models. The proposed ensemble approach provides improved prediction performance while reducing processing time compared to existing state-of-the-art solutions.