Robust 3-D Feature Extraction and Graph Pose-Invariant Matching for Contactless Fingerprint

Abstract

Contactless fingerprint has gained lots of attention in recent fingerprint studies. However, most existing contactless fingerprint algorithms treat contactless fingerprints as 2D plain fingerprints, which lack consideration of the modality difference between contactless and contact fingerprints, especially the intrinsic 3D features in contactless fingerprints. This paper proposes a novel contactless fingerprint recognition algorithm that captures the revealed 3D feature of contactless fingerprints rather than the plain 2D feature. The proposed method first recovers 3D features from the monocular input contactless fingerprint, including the 3D shape model and 3D fingerprint feature (minutiae, orientation, etc.). Then, a novel pose estimation and matching method based on 3D graph network is proposed according to the extracted 3D feature. The proposed method is able to perform robust 3D feature extractions on various contactless fingerprints across multiple finger poses and correcting the pose of contactless fingerprints in 3D space. The results of the experiments on contactless fingerprint databases show that the proposed method successfully improves the matching accuracy of contactless fingerprints. Exceptionally, our method performs stably across multiple poses of contactless fingerprints due to 3D pose regression and embeddings, which is a great advantage compared to 2D-based previous contactless fingerprint recognition algorithms.