Towards More Accurate Contactless Fingerprint Minutiae Extraction and Pose-Invariant Matching

Contactless fingerprint identification offers significantly higher user convenience, hygiene and has attracted increasing attention for the deployments. However, the presentation of fingers towards the contactless fingerprint sensors is hard to control and often results in unwanted pose changes that significantly degrade the contactless fingerprint matching accuracy. In order to address such problems and improve the fingerprint matching accuracy, this paper proposes a more precise minutiae extraction and pose-compensation approach. As compared with the conventional minutiae extraction approaches, our deep neural network-based approach does not require any image enhancement and is robust to spurious minutiae. All the minutiae extracted from our network are subjected to a three stage pose compensation framework: a) view angle estimation based on the location of core point, b) ellipsoid model formulation which simulates and compensate finger pose, c) intersection area estimation and alignment between different view angles. The proposed ellipsoid model is adaptive to both the silhouette of 2D contactless fingerprint image and the estimated view angle. The corresponding area between the different view angles can be theoretically estimated using this model and incorporated to align two contactless fingerprints for achieving superior matching accuracy. Our reproducible experimental results presented in this paper using public databases, and a database acquired during this work, validate the effectiveness of the proposed framework over the commercial software and earlier methods.