Automated spoof-detection for fingerprints using optical coherence tomography.

Fingerprint recognition systems are prevalent in high-security applications. As a result, the act of spoofing these systems with artificial fingerprints is of increasing concern. This research presents an automatic means for spoof-detection using optical coherence tomography (OCT). This technology is able to capture a 3D representation of the internal structure of the skin and is thus not limited to a 2D surface scan. The additional information afforded by this representation means that accurate spoof-detection can be achieved. Two features were extracted to detect the presence of (1) an additional thin layer on the surface of the skin and (2) a thicker additional layer or a complete artificial finger. An analysis of these features showed that they are highly separable, resulting in 100% accuracy regarding spoof-detection, with no false rejections of real fingers. This is the first attempt at fully automated spoof-detection using OCT.

Efficient internal and surface fingerprint extraction and blending using optical coherence tomography.

Optical coherence tomography provides a 3D representation of fingertip skin where surface and internal fingerprints are found. These fingerprints are topographically identical. However, the surface skin is prone to damage, distortion, and spoofing; and the internal fingerprint is difficult to access and extract. This research presents a novel scaling-resolution approach to fingerprint zone detection and extraction. Furthermore, a local-quality-based blending procedure is also proposed. The accuracy of the zone-detection algorithm is comparable to an earlier work, yielding a mean-squared error of 25.9 and structural similarity of 95.8% (compared to a ground-truth estimate). Blending the surface and internal fingerprints improved the National Institute of Science and Technology's Fingerprint Image Quality scores and the average maximum match scores (when matched against conventional surface counterparts). The fingerprint blending procedure was able to combine high-quality regions from both fingerprints, thus mitigating surface wrinkles and anomalous poor-quality regions. Furthermore, spoof detection via a surface-to-internal fingerprint comparison was proposed and tested.