Fringe projection profilometry has been applied to measure 3D information of fingertip and collect contactless 3D fingerprints. When low-resolution (LR) camera is used in the system due to reasons such as cost, the captured fringe patterns may appear blurry, which results in less obvious contrast between valleys and ridges in the reconstructed contactless 3D fingerprints. To address this issue, we introduce an unsupervised super-resolution (SR) method that solely relies on low-resolution fringe patterns. Our approach combines a two-loop generative adversarial network. In the forward loop, a binarized interpolation loss function is designed to ensure that the upsampling generator preserves ridge and valley details. In the backward loop, the discriminator ensures that the fringe patterns produced by the downsampling generator are both repeatable and similar to the original fringe patterns. Finally, the fringe patterns are reconstructed to obtain 3D fingerprints. Experimental results demonstrate the advantages of our proposed method.
In traditional fingerprint biometrics, fingertips stained with water, dust or fake fingerprints can lead to security issues such as false rejection and acceptance. It has been found in biology that the fingerprint pattern also exists in the papillary layer inside the fingertips, which is the source of the external epidermal fingerprint. Optical coherence tomography (OCT) is a noninvasive imaging technique that captures micrometer-resolution, three-dimensional images from within biological tissues. In this paper, a spectral domain OCT system is established to measure subcutaneous information from fingertips with large area and high resolution. A hybrid hierarchical clustering is proposed to identify the contours of the corneum stratum and papillary junction, with which the internal and external fingerprints can be extracted, respectively. The experimental results show that the external and internal fingerprints obtained by OCT have same pattern and almost same minutiae distribution. Thus the internal fingerprint can be a good replacement or complement of the external fingerprint, and the detection of internal fingerprint can defense against fake fingerprints.
Phase unwrapping is a vital part of optical measurement technology. The path-following method based on quality map, the mainstream technology in single fringe-pattern phase unwrapping, can suppress the error propagation of phase unwrapping effectively. However, the time consumption of this technology will increase significantly along with the increasing of pixels in a fringe pattern. For this reason, a phase unwrapping method based on region division was proposed, through bidimensional sinusoids-assisted empirical mode decomposition (BSEMD). In this method, the problematic regions where the phase unwrapping error is easy to occur such as object edge and local shadow will be divided. In these problematic regions, the quality-guided phase unwrapping method will be applied, where the instantaneous frequencies acquired in the process of extracting wrapped phase are taken as quality map. Then flood fill algorithm will be applied in the rest regions directly. Through simulation and experiment, the proposed method greatly improves the processing speed while ensuring the accuracy.
Fringe projection profilometry (FPP) has become one of the most popular 3D information acquisition techniques being developed over the past three decades. In FPP, phase unwrapping is a critical step in acquiring the correspondence information for 3D reconstruction. Unlike traditional FPP which uses methods such as temporal phase unwrapping, fast FPP measurement systems require phase unwrapping of single phase map probably with discontinuous objects. Phase unwrapping in fast FPP such brings three difficulties, the separation of discontinuous wrapped phases, the unwrapping of each wrapped phase and the linking of the discontinuous phase segments. During FPP measurement of multiple objects, their corresponding phase maps may locate in different part of the measurement image or side by side. Object separation based on the modulation values along could not well identify the discontinuous boundaries. In this paper, the object separation based on modulation is adapted first to remove the background and separate isolated wrapped phases. For each isolated wrapped phase, local orientation coherence based discontinuous phase separation is further used to identify the existence of discontinuous boundaries. After the segmentation, spatial unwrapping method is applied to each wrapped phase segments. In the last, the phase segments are linked using a base phase map. Experimental results show that our algorithm works well for multiple and overlapping object measurements.
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