Surface fingerprint scanners are limited to a two-dimensional representation of the fingerprint topography, and thus, are vulnerable to fingerprint damage, distortion, and counterfeiting. Optical coherence tomography (OCT) scanners are able to image (in three dimensions) the internal structure of the fingertip skin. Techniques for obtaining the internal fingerprint from OCT scans have since been developed. This research presents an internal fingerprint extraction algorithm designed to extract high-quality internal fingerprints from touchless OCT fingertip scans. Furthermore, it serves as a correlation study between surface and internal fingerprints. Provided the scanned region contains sufficient fingerprint information, correlation to the surface topography is shown to be good (74% have true matches). The cross-correlation of internal fingerprints (96% have true matches) is substantial that internal fingerprints can constitute a fingerprint database. The internal fingerprints’ performance was also compared to the performance of cropped surface counterparts, to eliminate bias owing to information level present, showing that the internal fingerprints’ performance is superior 63.6% of the time.
Optical coherence tomography (OCT) is a high-resolution imaging technology capable of capturing a three-dimensional (3-D) representation of fingertip skin. The papillary junction—a junction layer of skin containing the same topographical features as the surface fingerprint—is contained within this representation. The top edge of the papillary junction contains the topographical information pertinent to the internal fingerprint. Extracting the internal fingerprint from OCT fingertip scans has been shown to be possible. Currently, acquiring the internal fingerprint involves manually defining the region containing it. This manner of definition is inefficient. Perfect knowledge of the location of the papillary junction is hypothesized as achievable. This research details and tests a k-means clustering approach for papillary junction detection. All tested metrics are of a standard comparable to the measured human error. The technique presented in this research is highly successful in detection of the location of the papillary junction. Furthermore, high-quality internal fingerprints are acquired using the coordinates obtained.
Standard surface fingerprint scanners are vulnerable to counterfeiting attacks and also failure due to skin damage and distortion. Thus a high security and damage resistant means of fingerprint acquisition is needed, providing scope for new approaches and technologies. Optical Coherence Tomography (OCT) is a high resolution imaging technology that can be used to image the human fingertip and allow for the extraction of a subsurface fingerprint. Being robust toward spoofing and damage, the subsurface fingerprint is an attractive solution. However, the nature of the OCT scanning process induces speckle: a correlative and multiplicative noise. Six speckle reducing filters for the digital enhancement of OCT fingertip scans have been evaluated. The optimized Bayesian non-local means algorithm improved the structural similarity between processed and reference images by 34%, increased the signal-to-noise ratio, and yielded the most promising visual results. An adaptive wavelet approach, originally designed for ultrasound imaging, and a speckle reducing anisotropic diffusion approach also yielded promising results. A reformulation of these in future work, with an OCT-speckle specific model, may improve their performance.