Depth-independent internal fingerprint based on optical coherence tomography

Abstract

Optical coherence tomography (OCT) was used for imaging three-dimensional fingerprint to overcome the effects of different skin states and fake fingerprint. However, the OCT-based fingerprint features depend on the depth of fingertip skin which is still challenging for biometric recognition and encryption. In this work, we presented a new approach of maximum intensity projection (MIP) image of the epidermal-dermal junction (DEJ) to extract the internal fingerprint that is independent of the depth of fingertip skin. To begin with, the surface and DEJ were segmented based on the deep learning algorithm. Then the internal fingerprint was extracted by the MIP image of DEJ which has a more accurate structural similarity by quantitative analysis. The experimental results showed that internal fingerprint acquired by MIP of DEJ can be applied for scar-simulation fingertip and encryption since it is not sensitive to the states of surface skin and independent of the depth.