Multi-Surface Multi-Technique (MUST) Latent Fingerprint Database

Latent fingerprint recognition involves acquisition and comparison of latent fingerprints with an exemplar gallery of fingerprints. The diversity in the surface type leads to different procedures to recover the latent fingerprint. The appearance of latent fingerprints varies significantly due to the development techniques, leading to large intra-class variation. Due to lack of large datasets acquired using multiple mechanisms and surfaces, existing algorithms for latent fingerprints enhancement and comparison may perform poorly. In this study, we propose a Multi-Surface Multi-Technique (MUST) Latent Fingerprint Database. The database has more than 16,000 latent fingerprint impressions from 120 unique classes. Including corresponding exemplar fingerprints (livescan and rolled) and extended gallery, the dataset has nearly 21,000 impressions. It has latent fingerprints acquired under 35 different scenarios and additional four subsets of exemplar prints. With 39 different subsets, the database illustrates intra-class variations in latent fingerprints. The database can build robust algorithms for latent fingerprint enhancement, segmentation, comparison, and multi-task learning. We also provide annotations for manually marked minutiae, acquisition PPI, and semantic segmentation masks are also provided. We also present the proposed dataset's experimental protocol and baseline results. The availability of the proposed database can encourage research in handling intra-class variation in latent fingerprint recognition.