Alignment-Free Cross-Sensor Fingerprint Matching Based on the Co-Occurrence of Ridge Orientations and Gabor-HoG Descriptor

Abstract

The existing automatic fingerprint verification methods are designed to work under the assumption that the same sensor is installed for enrollment and authentication (regular matching). There is a remarkable decrease in efficiency when one type of contact-based sensor is employed for enrolment and another type of contact-based sensor is used for authentication (cross-matching or fingerprint sensor interoperability problem,). The ridge orientation patterns in a fingerprint are invariant to sensor type. Based on this observation, we propose a robust fingerprint descriptor called the co-occurrence of ridge orientations (Co-Ror), which encodes the spatial distribution of ridge orientations. Employing this descriptor, we introduce an efficient automatic fingerprint verification method for cross-matching problem. Further, to enhance the robustness of the method, we incorporate scale based ridge orientation information through Gabor-HoG descriptor. The two descriptors are fused with canonical correlation analysis (CCA), and the matching score between two fingerprints is calculated using city-block distance. The proposed method is alignment-free and can handle the matching process without the need for a registration step. The intensive experiments on two benchmark databases (FingerPass and MOLF) show the effectiveness of the method and reveal its significant enhancement over the state-of-the-art methods such as VeriFinger (a commercial SDK), minutia cylindercode (MCC), MCC with scale, and the thin-plate spline (TPS) model. The proposed research will help security agencies, service providers and law-enforcement departments to overcome the interoperability problem of contact sensors of different technology and interaction types.INDEX TERMS biometrics; fingerprint sensor interoperability; cross-sensor fingerprint matching; fingerprint verification; feature-level fusion

Empirical comparison of DSLRs and smartphone cameras for latent prints photography

“…VeriFinger is a software produced by Neurotechnology, which is based on the MegaMatcher identification engine (Neurotechnology, 2020) and compliant with NIST MINEX (Grother, 2006). It has been widely adopted worldwide including airports, law enforcement AFIS systems, and childcare identification systems (Neurotechnology, VeriFinger SDK, 2020), and also been utilized in many scientific studies (Laurent Beslay, 2018; AlShehri, 2018; Nguyen, 2019). VeriFinger can inspect a fingerprint image and calculate its quality with a numerical score ranging from 0 to 100 (Fingerprint Image Quality Score).…”

Cross-Sensor Fingerprint Matching Using Siamese Network and Adversarial Learning

“…However, this problem has been studied by a few researchers. The existing methods are based on hand-engineered features [ 4 ], nonlinear distortions [ 5 , 6 ], and scaling of fingerprints [ 7 , 8 , 9 ]. Despite these efforts, the interoperability problem is still challenging.…”

“…This observation motivated us to propose a new method based on deep learning and explore its effect on the cross-matching problem. In view of this, we introduce a cross-sensor matching method using Siamese network trained with adversarial learning [ 4 ]. The Siamese network, adopting the convolutional neural network as a backbone, is trained with Gabor-HoG descriptor [ 4 ] to learn the correspondences.…”

“…In view of this, we introduce a cross-sensor matching method using Siamese network trained with adversarial learning [ 4 ]. The Siamese network, adopting the convolutional neural network as a backbone, is trained with Gabor-HoG descriptor [ 4 ] to learn the correspondences. The Gabor-HoG descriptor which encodes multi-scale local ridge structures and changes across ridge patterns.…”

“…Some methods based on hand-engineered features employ traditional features such as: Gabor-HoG, Co-occurrence of ridge orientations (Co-RoR), and local binary patterns (LBPs) [ 14 ]. Helala et al [ 4 ] introduced the crossVFinger method to solve the cross-sensor fingerprint matching; they designed a novel feature descriptor called Co-occurrence of ridge orientations (Co-RoR)to take into consideration the ridge orientation field for increasing the robustness of the crossVFinger. The Co-RoR fused with Gabor-HoG descriptor using Canonical Correlation Analysis(CCA).…”