  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 cylinder-code (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.
