Cross‐domain fingerprint recognition remains difficult when latent impressions and smartphone finger photos are matched against contact‐based sensor templates. We study this problem in the latent fingerprints in the wild (LFIW) database. We benchmark five representative matchers on eight latent‐ and finger‐photo‐to‐sensor protocols. The results confirm a clear gap: no single matcher is robust across all domains. We then introduce two domain‐aware components. First, domain‐normalized quality score (DNQS) standardizes existing quality metrics within each domain. This reduces domain bias and improves the agreement between quality and verification error. Second, quality‐weighted score fusion (QWSF) uses DNQS to fuse two complementary matchers, MCC and Neurotechnology. QWSF reduces errors across all protocols. It achieves a 15% relative equal error rate (EER) reduction on iPad vs. Opt and a 7% reduction on average. Paired bootstrap confidence intervals support the reliability of the gains. Improvements also hold at a low‐FMR operating point (FNMR at FMR = 10 −3 ). These findings show that quality normalization and quality‐aware fusion can improve cross‐domain robustness without retraining matchers.
Liu et al. (Thu,) studied this question.