Matching optical coherence tomography fingerprint scans using an iterative closest point pipeline
Identifying people from their fingerprints is based on well established technology. However, a number of challenges remain, notably overcoming the low feature density of the surface fingerprint and suboptimal feature matching. 2D contact based fingerprint scanners offer low security performance, are easy to spoof, and are unhygienic. Optical Coherence Tomography (OCT) is an emerging technology that allows a 3D volumetric scan of the finger surface and its internal microstructures. The junction between the epidermis and dermis - the internal fingerprint - mirrors the external fingerprint. The external fingerprint is prone to degradation from wear, age, or disease. The internal fingerprint does not suffer these deficiencies, which makes it a viable candidate zone for feature extraction. We develop a biometrics pipeline that extracts and matches features from and around the internal fingerprint to address the deficiencies of contemporary 2D fingerprinting. Eleven different feature types are explored. For each type an extractor and Iterative Closest Point (ICP) matcher is developed. ICP is modified to operate in a Cartesiantoroidal space. Each of these features are matched with ICP against another matcher, if one existed. The feature that has the highest Area Under the Curve (AUC) on an Receiver Operating Characteristic of 0.910 is a composite of 3D minutia and mean local cloud, followed by our geometric properties feature, with an AUC of 0.896. By contrast, 2D minutiae extracted from the internal fingerprint achieved an AUC 0.860. These results make our pipeline useful in both access control and identification applications. ICP offers a low False Positive Rate and can match ∼30 composite 3D minutiae a second on a single threaded system, which is ideal for access control. Identification systems require a high True Positive and True Negative Rate, in addition time is a less stringent requirement. New identification systems would benefit from the introduction of an OCT based pipeline, as all the 3D features we tested provide more accurate matching than 2D minutiae. We also demonstrate that ICP is a viable alternative to match traditional 2D features (minutiae). This method offers a significant improvement over the popular Bozorth3 matcher, with an AUC of 0.94 for ICP versus 0.86 for Bozorth3 when matching a highly distorted dataset generated with SFinGe. This compatibility means that ICP can easily replace other matchers in existing systems to increase security performance.
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