Fingerprint Reconstruction Research Articles

Quasi-bound states-driven terahertz molecular fingerprint retriever

Limited by the weak resonant intensities of ordinary modes, the sensitivity of terahertz sensing is often insufficient to retreive trace molecular fingerprint features. Here, a quasi-bound states in the continuum-induced terahertz metasurface with high field enhancement is proposed to achieve trace molecular fingerprint sensing. The ratio of radiative loss to intrinsic loss of proposed metasurface can be accurately tailored by controlling in-plane symmetry, enabling working in the under-coupled, critically coupled and over-coupled regions. Based on the proposed metasurface, a pixelated metasensor has been designed for molecular fingerprint reconstruction, where negative, near-zero, and positive signal modulations can be observed in three different coupling regions. Finally, we demonstrate that the designed pixelated metasensor can achieve quantitative sensing of mixtures, providing an alternative technique for detecting and identifying trace molecules.

Identification of Distorted Fingerprints Using Wavelet Method and Convolutional Neural Network (CNN)

Biometrics offers a valuable tool for disaster victim identification, particularly through fingerprints. However, distorted or damaged fingerprints pose a significant challenge for recognition. This study explores the potential of Wavelet and Convolutional Neural Network (CNN) techniques to enhance the accuracy of distorted fingerprint recognition. Wavelet transform addresses the non-stationary nature of images and reduces detected noise. Convolutional Autoencoder, a CNN component, generates simplified feature representations from input images and attempts to reconstruct them. Utilizing 500 fingerprint samples, the testing results demonstrate accuracy variations ranging from 11% to 59.2%. Image reconstruction achieved 7.16% to 12.47% accuracy, while fingerprint matching attained accuracies between 92.71% and 93.96%. Averaging across all damage levels, the overall accuracy reached 37.65%, with average fingerprint reconstruction at 9.31% and average matching accuracy at 93.03%. The successful reconstruction and matching of distorted fingerprints within a certain range of damage using Wavelet and Convolutional Neural Network highlights the promising potential of these techniques for improved fingerprint identification in forensic and security contexts.

Fingerprint Reconstruction: Approaches to Improve Fingerprint Images

Fingerprint reconstruction methods have been initially proposed to spoof the fingerprint identification systems, wherein the fingerprints are generated from the fingerprint features stored in the database for template matching/identification purpose. The reconstructed fingerprints attempt to validate in the absence of the user/person. The poor fingerprint Images with scratches on fingerprint image or latent fingerprints or overlapping fingerprints shall also be reconstructed for personality identification. In this paper we discuss the two fingerprint reconstruction methods, one which uses minutiae features for reconstruction and the other one uses deep learning methods to reconstruct the fingerprint images. The poor fingerprint image which fails to validate the identity due to various reasons like poor skin condition/large cuts on the fingers/wet fingers/poor scanning of images shall be reconstructed for increasing the matching accuracy. The requirement of performance measure parameters used for evaluation of these systems are equal error rate, false acceptance rate, false rejection rate and average matching score. The deep learning methods are more suitable for reconstructing the fingerprint images that appear damaged due to poor skin condition/large cuts on the fingers/wet fingers/poor scanning of images. In terms of matching score comparison, the deep learning methods have matching scores in between 23-94% whereas for minutiae-based techniques the matching score is between 82 and 99.99%. The other performance parameter is the equal error rate (ERR) required to meet has to be closer to 0. The matching score is computed with the assumptions of false acceptance rate (FAR) ranging from 1% to 0%.

A uniform representation model for OCT-based fingerprint presentation attack detection and reconstruction

In current Optical Coherence Tomography (OCT)-based fingerprint recognition systems, Presentation Attack Detection (PAD) and subsurface fingerprint reconstruction are treated as two independent branches, resulting in high computation and system building complexity. Therefore, this paper proposes a uniform representation model for simultaneous PAD and subsurface fingerprint reconstruction. A novel semantic segmentation network using attention mechanisms was designed to extract and segment multiple subsurface structures from real finger slices (i.e., B-scans). The latent codes derived from the network are directly used to effectively detect PA because they contain abundant subsurface biological information, which is independent of PA materials and has strong robustness for unknown PAs. Segmented subsurface structures were adopted to reconstruct multiple subsurface 2D fingerprints. Extensive experiments were carried out on an in-house database, which is the largest public OCT-based fingerprint database with 2449 volumes. PAD performance was evaluated by comparing the results of the existing methods with those of other segmentation networks. The proposed uniform representation model can obtain an accuracy (Acc) of 96.63%, which achieves a state-of-the-art performance. The effectiveness of subsurface reconstruction was evaluated from the segmentation and recognition results. In the segmentation experiments, the proposed method achieved the best results with an Intersection of Union (mIOU) of 0.834 and a Pixel Accuracy of 0.937. By comparing the recognition performance on surface 2D fingerprints (e.g., commercial and high-resolution), the lowest results with an Equal Error Rate (EER) of 2.25% by minutiae matching and an EER of 5.42% by pore matching are achieved, which indicates the excellent reconstruction capability of the proposed uniform representation model.

Monocular 3D Fingerprint Reconstruction and Unwarping.

Compared with contact-based fingerprint acquisition techniques, contactless acquisition has the advantages of less skin distortion, more complete fingerprint area, and hygienic acquisition. However, perspective distortion is a challenge in contactless fingerprint recognition, which changes the ridge frequency and relative minutiae location, and thus degrades the recognition accuracy. We propose a learning-based shape-from-texture algorithm to reconstruct a 3-D finger shape from a single image and unwarp the raw image to suppress the perspective distortion. Our experimental results for 3-D reconstruction on contactless fingerprint databases show that the proposed method has high 3-D reconstruction accuracy. Experimental results for contactless-to-contactless and contactless-to-contact-based fingerprint matching indicate that the proposed method can improve the matching accuracy.

Bloch simulator-driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging.

To develop a unified deep-learning framework by combining an ultrafast Bloch simulator and a semisolid macromolecular magnetization transfer contrast (MTC) MR fingerprinting (MRF) reconstruction for estimation of MTC effects. The Bloch simulator and MRF reconstruction architectures were designed with recurrent neural networks and convolutional neural networks, evaluated with numerical phantoms with known ground truths and cross-linked bovine serum albumin phantoms, and demonstrated in the brain of healthy volunteers at 3 T. In addition, the inherent magnetization-transfer ratio asymmetry effect was evaluated in MTC-MRF, CEST, and relayed nuclear Overhauser enhancement imaging. A test-retest study was performed to evaluate the repeatability of MTC parameters, CEST, and relayed nuclear Overhauser enhancement signals estimated by the unified deep-learning framework. Compared with a conventional Bloch simulation, the deep Bloch simulator for generation of the MTC-MRF dictionary or a training data set reduced the computation time by 181-fold, without compromising MRF profile accuracy. The recurrent neural network-based MRF reconstruction outperformed existing methods in terms of reconstruction accuracy and noise robustness. Using the proposed MTC-MRF framework for tissue-parameter quantification, the test-retest study showed a high degree of repeatability in which the coefficients of variance were less than 7% for all tissue parameters. Bloch simulator-driven, deep-learning MTC-MRF can provide robust and repeatable multiple-tissue parameter quantification in a clinically feasible scan time on a 3T scanner.

End-to-End Surface and Internal Fingerprint Reconstruction From Optical Coherence Tomography Based on Contour Regression

Optical coherence tomography (OCT), as a non-destructive and high-resolution imaging technique, has been used to collect 3D fingertip data, which contains surface and internal fingerprints. Methods have been proposed for OCT fingerprint reconstruction. However, these methods have complex processing flow and are time consuming. In this paper, an end-to-end convolutional neural network based surface and internal fingerprint reconstruction method is proposed. A simple yet effective contour regression module is proposed and integrated in the network for direct estimation of contours of stratum corneum and viable epidermis junction from noisy OCT volume data, thus greatly simplify the processing flow. The proposed network further integrates multi-task learning with conventional segmentation task as auxiliary task and contour regression task as main task to facilitate the feature extraction and improve the robustness of the network. Depthwise separable convolution is adapted to a light-weight network for network computation complexity reduction. To the best of our knowledge, it is the first time that an end-to-end method is proposed for surface and internal fingerprint extraction from noisy OCT volume data. Experiments and comparisons are carried out in terms of contour estimation accuracy, fingerprint quality, fingerprint matching performance and computation efficiency. Compared with conventional method, the proposed method utilizes only 6% of original network parameters and 0.7% of original computation time, but achieves comparably results. Fingerprint by depth proves the accuracy and robustness of contour regression than pixel-wise layer segmentation. The proposed method is noise-insensitive, process-simple and time-efficient for OCT fingerprint reconstruction, which is significant for real time application in Automated Fingerprint Recognition Systems.

The Reconstruction of Human Fingerprints From High-Resolution Computed Tomography Data: Feasibility Study and Associated Ethical Issues.

The Reconstruction of Human Fingerprints From High-Resolution Computed Tomography Data: Feasibility Study and Associated Ethical Issues.

Ultrasonic Guided Wave Inversion Based on Deep Learning Restoration for Fingerprint Recognition.

As an established biometric authentication approach, fingerprint scanning has received considerable attention due to its high accuracy and reliability. In this article, the fingerprint reconstruction at any position is achieved in large physical domains, which monitors wavefield variations of plate-like structures within arrays through the ultrasonic guided wave. Accurate reconstruction and quantitative characterization of fingerprints are obtained using fast inversion tomography (FIT) based on the deep learning convolutional neural network (DLCNN). Parametric optimization is conducted to reveal submillimeter fingerprint minutiae, and a specific DLCNN model is proposed for the artifact removal in FIT reconstructions. The results prove that the FIT based on DLCNN restoration can significantly improve the imaging quality in terms of increased resolution, reduced reconstruction errors, and higher fingerprint matching confidence. The reconstruction also allows an exponential improvement in computational efficiency as a result of much-reduced sensor numbers. Several factors affecting the performance of the proposed reconstruction method are discussed at the end.

Multicomponent MR fingerprinting reconstruction using joint-sparsity and low-rank constraints.

To develop an efficient algorithm for multicomponent MR fingerprinting (MC-MRF) reconstructions directly from highly undersampled data without making prior assumptions about tissue relaxation times and expected number of tissues. The proposed method reconstructs MC-MRF maps from highly undersampled data by iteratively applying a joint-sparsity constraint to the estimated tissue components. Intermediate component maps are obtained by a low-rank multicomponent alternating direction method of multipliers (MC-ADMM) including the non-negativity of tissue weights as an extra regularization term. Over iterations, the used dictionary compression is adjusted. The proposed method (k-SPIJN) is compared with a two-step approach in which image reconstruction and multicomponent estimations are performed sequentially and tested in numerical simulations and in vivo by applying different undersampling factors in eight healthy volunteers. In the latter case, fully sampled data serves as the reference. The proposed method shows improved precision and accuracy in simulations compared with a state-of-art sequential approach. Obtained in vivo magnetization fraction maps for different tissue types show reduced systematic errors and reduced noise-like effects. Root mean square errors in estimated magnetization fraction maps significantly reduce from 13.0% 5.8% with the conventional, two-step approach to 9.6% 3.9% and 9.6% 3.2% with the proposed MC-ADMM and k-SPIJN methods, respectively. Mean standard deviation in homogeneous white matter regions reduced significantly from 8.6% to 2.9% (two step vs. k-SPIJN). The proposed MC-ADMM and k-SPIJN reconstruction methods estimate MC-MRF maps from highly undersampled data resulting in improved image quality compared with the existing method.

High-Quality MR Fingerprinting Reconstruction Using Structured Low-Rank Matrix Completion and Subspace Projection.

Due to the capability of fast multiparametric quantitative imaging, magnetic resonance fingerprinting (MRF) is becoming a promising quantitative magnetic resonance imaging approach. However, the artifacts caused by the highly undersampled data acquisition lead to inaccurate estimation of the tissue parameter maps. Based on the assumption that the 3-D MRF data can be modeled as a piecewise smooth signal, with the discontinuities localized to the zero sets of a bandlimited function, we exploit the low-rank property of the structured Toeplitz matrix constructed from the Fourier measurements. In addition, we adopt the subspace projection scheme to improve the accuracy of parameter estimation. In order to efficiently solve the regularized problem, we propose an iterative two-stage algorithm, which alternately updates the k -space data and projects the space-time matrix into the dictionary space. Numerical experiments demonstrate that the proposed algorithm shows significant improvement in MRF time-series images reconstruction and can provide more accurate parameter maps over the state-of-the-art algorithms.

Decomposing compounds enables reconstruction of interaction fingerprints for structure-based drug screening

BackgroundStructure-based drug repositioning has emerged as a promising alternative to conventional drug development. Regardless of the many success stories reported over the past years and the novel breakthroughs on the AI-based system AlphaFold for structure prediction, the availability of structural data for protein–drug complexes remains very limited. Whereas the chemical libraries contain millions of drug compounds, the vast majority of them do not have structures to crystallized targets,and it is, therefore, impossible to characterize their binding to targets from a structural view. However, the concept of building blocks offers a novel perspective on the structural problem. A drug compound is considered a complex of small chemical blocks or fragments, which confer the relevant properties to the drug and have a high proportion of functional groups involved in protein binding. Based on this, we propose a novel approach to expand the scope of structure-based repositioning approaches by transferring the structural knowledge from a fragment to a compound level.ResultsWe fragmented over 100,000 compounds in the Protein Data Bank (PDB) and characterized the structural binding mode of 153,000 fragments to their crystallized targets. Using the fragment’s data, we were able to artificially reconstruct the binding mode of over 7,800 complexes between ChEMBL compounds and their known targets, for which no structural data is available. We proved that the conserved binding tendency of fragments, when binding to the same targets, highly influences the drug’s binding specificity and carries the key information to reconstruct full drugs binding mode. Furthermore, our approach was able to reconstruct multiple compound-target pairs at optimal thresholds and high similarity to the actual binding mode.ConclusionsSuch reconstructions are of great value and benefit structure-based drug repositioning since they automatically enlarge the technique’s scope and allow exploring the so far ‘unexplored compounds’ from a structural perspective. In general, the transfer of structural information is a promising technique that could be applied to any chemical library, to any compound that has no crystal structure available in PDB, and even to transfer any other feature that may be relevant for the drug discovery process and that due to data limitations is not yet fully available. In that sense, the results of this work document the full potential of structure-based screening even beyond PDB.Graphical

Acceleration of Magnetic Resonance Fingerprinting Reconstruction Using Denoising and Self-Attention Pyramidal Convolutional Neural Network.

Magnetic resonance fingerprinting (MRF) based on echo-planar imaging (EPI) enables whole-brain imaging to rapidly obtain T1 and T2* relaxation time maps. Reconstructing parametric maps from the MRF scanned baselines by the inner-product method is computationally expensive. We aimed to accelerate the reconstruction of parametric maps for MRF-EPI by using a deep learning model. The proposed approach uses a two-stage model that first eliminates noise and then regresses the parametric maps. Parametric maps obtained by dictionary matching were used as a reference and compared with the prediction results of the two-stage model. MRF-EPI scans were collected from 32 subjects. The signal-to-noise ratio increased significantly after the noise removal by the denoising model. For prediction with scans in the testing dataset, the mean absolute percentage errors between the standard and the final two-stage model were 3.1%, 3.2%, and 1.9% for T1, and 2.6%, 2.3%, and 2.8% for T2* in gray matter, white matter, and lesion locations, respectively. Our proposed two-stage deep learning model can effectively remove noise and accurately reconstruct MRF-EPI parametric maps, increasing the speed of reconstruction and reducing the storage space required by dictionaries.

A Novel Minutiae Triangulation Technique for Non-invertible Fingerprint Template Generation

The fingerprint is the readily available and most widely used biometrics for the security of a system. In order to impart the security of the template database, it is important to make the fingerprint template robust to fingerprint reconstruction, i.e., invertibility. In this paper, a fingerprint template is proposed using a novel minutiae triangulation technique. In this triangulation technique, the set of minutiae of a template is taken to generate a set of minutiae triplets each forming a triangle. After this, a minutiae transformation is applied to obtain a set of triplets of internal angles of the triangle which also removes the minutiae location and orientation information. In the absence of this information, the orientation map of the fingerprint cannot be estimated. This set of triplets together with some other information is used as a proposed fingerprint template. The proposed system is experimented using standard FVC2000, FVC2004 and FVC2006 databases and yields better results in terms of EER (Equal Error Rate) as compared with the state-of-art techniques.

Single Photon Avalanche Diode Arrays for Time-Resolved Raman Spectroscopy.

The detection of peaks shifts in Raman spectroscopy enables a fingerprint reconstruction to discriminate among molecules with neither labelling nor sample preparation. Time-resolved Raman spectroscopy is an effective technique to reject the strong fluorescence background that profits from the time scale difference in the two responses: Raman photons are scattered almost instantaneously while fluorescence shows a nanoseconds time constant decay. The combination of short laser pulses with time-gated detectors enables the collection of only those photons synchronous with the pulse, thus rejecting fluorescent ones. This review addresses time-gating issues from the sensor standpoint and identifies single photon avalanche diode (SPAD) arrays as the most suitable single-photon detectors to be rapidly and precisely time-gated without bulky, complex, or expensive setups. At first, we discuss the requirements for ideal Raman SPAD arrays, particularly focusing on the design guidelines for optimized on-chip processing electronics. Then we present some existing SPAD-based architectures, featuring specific operation modes which can be usefully exploited for Raman spectroscopy. Finally, we highlight key aspects for future ultrafast Raman platforms and highly integrated sensors capable of undistorted identification of Raman peaks across many pixels.

3D Fingerprint Recognition based on Ridge-Valley-Guided 3D Reconstruction and 3D Topology Polymer Feature Extraction.

An automated fingerprint recognition system (AFRS) for 3D fingerprints is essential and highly promising for biometric security. Despite the progress in developing 3D AFRSs, achieving high-quality real-time reconstruction and high-accuracy recognition of 3D fingerprints remain two challenging issues. To address them, we propose a robust 3D AFRS based on ridge-valley (RV)-guided 3D fingerprint reconstruction and 3D topology polymer (TTP) feature extraction. The former considers the unique fingerprint characteristics of the RV and achieves real-time reconstruction. Unlike traditional triangulation-based methods that establish correspondences between points by cross-correlation-based searching, we propose to establish RV correspondences (RVCs) between ridges/valleys by defining and calculating a RVC matrix based on the topology of RV curves. To enhance depth reconstruction, curve-based smoothing is proposed to refine our novel RV disparity map. The TTP feature codes the 3D topology by projecting the 3D minutiae onto multiple planes and extracting their corresponding 2D topologies and has proven to be effective and efficient for 3D fingerprint recognition. Comprehensive experimental results demonstrate that our method outperforms the state-of-the-art methods in terms of both reconstruction and recognition accuracy. Also, due to its very short running time, it is appropriate for practical applications.

基于稀疏训练点和指纹重构的室内可见光三维定位算法

基于稀疏训练点和指纹重构的室内可见光三维定位算法

A Flexible Touch-Based Fingerprint Acquisition Device and a Benchmark Database Using Optical Coherence Tomography

Optical coherence tomography (OCT) is a high-resolution imaging technology probing the internal structure of multilayered tissues. Since it provides subsurface fingerprint information that is identical to the surface texture but unaffected by any surface defects, OCT-based fingerprints open up a new domain for establishing robust and high-security automatic fingerprint identification systems (AFISs). However, the development of OCT-based fingerprint recognition is hindered by the lack of public OCT-based fingerprint database for algorithm analysis and testing. This article, for the first time, established an OCT-based fingerprint database with thousands of fingers using our custom-built acquisition device. The website of this data set is https://github.com/CV-SZU/. Moreover, the images included in the database were selected after quality evaluation based on image resolution, image size, effective measured area, and the number of extractable features. Finally, case studies, including antispoofing, multiple subsurface fingerprint reconstruction, and fingerprint verification, were discussed based on the developed database. The database can serve as a benchmark for developing effective antispoofing, live detection, and high-accurate fingerprint recognition algorithms. It will significantly promote the research in the area of fingerprint biometric and will also advance the development of commercial products.

Surface and Internal Fingerprint Reconstruction From Optical Coherence Tomography Through Convolutional Neural Network

Optical coherence tomography (OCT), as a non-destructive and high-resolution fingerprint acquisition technology, is robust against poor skin conditions and resistant to spoof attacks. It measures fingertip information on and beneath skin as 3D volume data, containing the surface fingerprint, internal fingerprint and sweat glands. Various methods have been proposed to extract internal fingerprints, which ignore the inter-slice dependence and often require manually selected parameters. In this article, a modified U-Net that combines residual learning, bidirectional convolutional long short-term memory and hybrid dilated convolution (denoted as BCL-U Net) for OCT volume data segmentation and two fingerprint reconstruction approaches are proposed. To the best of our knowledge, it is the first time that simultaneous and automatic extraction is performed for surface fingerprint, internal fingerprint and sweat gland. The proposed BCL-U Net utilizes the spatial dependence in OCT volume data and deals with segmentation of objects with diverse sizes to achieve accurate extraction. Comparisons have been performed to demonstrate the advantages of the proposed method. A thorough evaluation of the recognition abilities of internal and surface fingerprints is conducted using a dataset significantly larger than previous studies. Four databases containing internal and surface fingerprints are generated from 1572 OCT volume data by the proposed method. The internal fingerprint matching experiment has achieved a lowest equal error rate (EER) of 0.95%. Mixed internal and surface fingerprint matching experiment is also performed and achieves an EER of 3.67%, verifying the consistency of the internal and surface fingerprints. The matching experiments for fingers under poor skin conditions show a 2.47% EER of internal fingerprints that is much lower than that of surface fingerprints, which proves the advantage of internal fingerprints and indicates the potential of the internal fingerprints to supplement or replace the surface fingerprints for some specific applications.

Further Development of Subspace Imaging to Magnetic Resonance Fingerprinting: A Low-rank Tensor Approach.

Magnetic resonance fingerprinting is a recent quantitative MRI technique that simultaneously acquires multiple tissue parameter maps (e.g., T1, T2, and spin density) in a single imaging experiment. In our early work, we demonstrated that the low-rank/subspace reconstruction significantly improves the accuracy of tissue parameter maps over the conventional MR fingerprinting reconstruction that utilizes simple pattern matching. In this paper, we generalize the low-rank/subspace reconstruction by introducing a multilinear low-dimensional image model (i.e., a low-rank tensor model). With this model, we further estimate the subspace associated with magnetization evolutions to simplify the image reconstruction problem. The proposed formulation results in a nonconvex optimization problem which we solve by an alternating minimization algorithm. We evaluate the performance of the proposed method with numerical experiments, and demonstrate that the proposed method improves the conventional reconstruction method and the state-of-the-art low-rank reconstruction method.