Minutiae Attention Network With Reciprocal Distance Loss for Contactless to Contact-Based Fingerprint Identification

Abstract

Interoperability between contactless and conventional contact-based fingerprint recognition systems is fundamental for the success of emerging contactless fingerprint technologies which are highly sought, especially due to current pandemic. However, image formation differences and acquisition distortions between these two modalities pose significant challenges for such interoperability. In order to address these challenges, this paper presents a minutiae attention network with Siamese architecture and the reciprocal distance loss function to enable more accurate contactless to contact-based fingerprint identification. The proposed network contains two branches, a global-net branch to recover global features and a minutiae attention branch that focuses on the local minutiae areas. Attention mechanism is introduced to guide the minutiae attention branch to concentrate on distorted areas and recover minutiae/features correspondence for contactless and contact-based fingerprint images from the same fingers. Meanwhile, reciprocal distance loss is specifically designed to impose strong penalty towards contactless and contact-based fingerprint images from different fingers and guide the network to learn robust features for distinguishing identities. Experimental results on two publicly available databases illustrate significant performance improvements, over state-of-art methods in the literature, and validate the effectiveness of the proposed framework for the contactless to contact-based fingerprint identification.