CaS2M: A Calibrated Single-to-Multiple Framework for Real-World Partial Fingerprint Recognition

Ziyang Gao, Xianglong Li, Guan Wang, Tianfan Peng, Jingjing Chang, Yixin Yang, Hai-Bao Chen

Abstract: With the reducing size of fingerprint collection modules in mobile devices, partial fingerprints are increasingly characterized by smaller overlapping areas and higher self-similarity. Existing methods either aggregate similarity scores from individual Single-to-Single recognition or directly employ a Single-to-Multiple network to verify the match between the query and templates. However, these methods either lack sufficient interaction between templates, or fail to provide adequate supervision for the alignment process, a crucial step in fingerprint recognition, thereby limiting overall accuracy. In this paper, we propose a novel partial fingerprint recognition strategy termed Calibrated Single-to-Multiple (CaS2M), which first calibrates template fingerprints individually, then combines them with the query fingerprint in a matcher network for feature fusion. Building upon this strategy, we develop a dual-stage framework tailored to real-world applications. During enrollment, a lightweight patch-based feature indexing algorithm and a template selection strategy are employed accounting for limited hardware resources. For authentication, independent calibration is first applied, followed by an attention-based matcher network to verify identity consistency. Experimental results on multiple public datasets (NIST 302, NIST SD4, SpoofGAN, FVC2002 DB1A & DB3A) and a self-build dataset demonstrate that our framework achieves superior performance over state-of-the-art algorithms, providing new insights for multi-template partial fingerprint recognition.

External IDs:dblp:journals/tifs/GaoLWPCYC25