Query2Set: Single-to-Multiple Partial Fingerprint Recognition Based on Attention Mechanism
Shengjie Chen, Zhenhua Guo, Xiu Li, Dongliang Yang
Abstract
Currently, fingerprint authentication systems in mobile devices, which have limited-size fingerprint sensors, are mainly based on partial fingerprint matching algorithms. To cover all areas of the finger, the system usually collects multiple partially overlapping partial fingerprints during the enrollment. Existing recognition methods either perform score-level fusion after single-to-single matching, or perform single-to-single matching after image-level mosaicking. However, these two-stage methods have the risk of discarding some real information or introducing some fake information. In this paper, we define this “query2set” task and propose a novel single-to-multiple partial fingerprint recognition method based on atttention mechanism. Our end-to-end deep model can adaptively extract and fuse appropriate features from a set of fingerprints for matching based on the input query fingerprint. Experiments indicate that our method outperforms several state-of-the-art single-to-single approaches and provides a new insight of fingerprint recognition on mobile devices.