High-resolution Fingerprint Research Articles

Design of a biomimetic cellulose nanofibre-based double information encryption sensor for fingerprint imaging

The development of double encryption system enables information to switch reversibly between “False” and “True”, which helps to ensure information security in the transmission process. Herein, a biomimetic cellulose nanofibre-based double information encryption sensor (CNF-DIES) with an excellent pH response and fluorescence colour-switching performance was prepared with fluorescein isothiocyanate and protoporphyrin IX modified acetylated cellulose nanofibres (ACNF) as the pH response switch and background, respectively. Interestingly, with the addition of cellulose, CNF-DIES can be regarded as both a dye and an ink binder, which can realize direct writing function. The fluorescein grafted to ACNF guaranteed the stability of writing and avoided the “coffee ring” phenomenon. The handwriting written by CNF-DIES processes excellent light/pH double encryption performance. Besides, the film prepared by CNF-DIES can realize high resolution fingerprint imaging. This work demonstrated a strategy for pH-responsive colour-tunable materials for sensors and double information encryption.

Excitation-independent deep-blue emitting carbon dots with 62% emission quantum efficiency and monoexponential decay profile for high-resolution fingerprint identification

Reaching emissive nanomaterials at short wavelengths with a high quantum efficiency (QE) is an attractive task for researchers. This is more demanding in carbon dots (CDs) with diverse applications that usually emit photons at wavelengths around 450–620 nm. In this study, deep blue-emissive doped-CDs (d-CDs) with high photoluminescence (PL) QE up to 62% and excitation-independent properties were prepared via a short-time microwave irradiation method. The prepared CDs showed simultaneous amorphous and crystalline features, with average sizes of 4.75 nm and bright emission color located at 422 nm. It was found that the presence of sulfur-related dopant levels plays a key role in emission properties in such a way that the PL signal drops significantly in the absence of N-acetyl-l-cysteine (NAC) as a dopant source. On the other hand, the trisodium citrate dihydrate (TSC) was selected as a carbon source to form the main carbon skeleton without it no emission was recorded. Monoexponential-fitted recombination trend with an average lifetime of about 10 ns also confirmed excellent PL emission properties with uniform energy levels and minimized defect-contributing recombinations. The practical use of the as-prepared N, S-doped CDs was assessed in fingerprint detection indicating a bright and clear scheme for both core and termination regions of the fingerprint. Simplicity, cost-effectiveness, high-product yield, low toxicity, along with high/stable PL quantum efficiency in deep-blue wavelengths, and demonstrated ability for fingerprint purposes, support the prospective application of these dual doped-CDs for sensing and bioimaging applications.

A Novel High-Resolution Fingerprint Representation Method

Current high-resolution fingerprints are almost entirely represented by sweat pores, resulting in information loss and weak robustness. This is caused by dividing fingerprint representation into pore detection and pore representation. This paper proposes a novel high-resolution fingerprint representation method for recognition based on only one fully convolutional network. By guiding the network to learn the most discriminative representation (i.e., sweat pores) and to reconstruct the original fingerprint image, pore maps and hidden features are provided simultaneously to represent the high-resolution fingerprint image for the subsequent direct matching process. The experimental results, which are evaluated on the public PolyU pore database, show that the best average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{T}$ </tex-math></inline-formula> , which is 94.88%, can be obtained using our proposed method. In addition, the recognition results show that our proposed method achieves matching equal error rates (EERs) of 5.55% and 1.27% on the PolyU DBI and DBII databases, respectively, which is better than other pore detection methods when using the same matching method. Compared with the latest DeepPoreID method which split fingerprint representation into pore extraction and pore representation, our approach improves the EER by up to 50.98% when evaluated on an in-house database, further demonstrating the outstanding generalization ability of the proposed method.

Site-specific protein methyl deuterium quadrupolar patterns by proton-detected 3D 2H-13C-1H MAS NMR spectroscopy.

Determination of protein structure and dynamics is key to understand the mechanism of protein action. Perdeuterated proteins have been used to obtain high resolution/sensitivty NMR experiments via proton-detection. These methods utilizes 1H, 13C and 15N nuclei for chemical shift dispersion or relaxation probes, despite the existing abundant deuterons. However, a high-sensitivity NMR method to utilize deuterons and e.g. determine site-specific deuterium quadrupolar pattern information has been lacking due to technical difficulties associated with deuterium's large quadrupolar couplings. Here, we present a novel deuterium-excited and proton-detected three-dimensional 2H-13C-1H MAS NMR experiment to utilize deuterons and to obtain site-specific methyl 2H quadrupolar patterns on detuterated proteins for the first time. A high-resolution fingerprint 1H-15N HSQC-spectrum is correlated with the anisotropic deuterium quadrupolar tensor in the third dimension. Results from a model perdeuterated protein has been shown.

Cross-Sensor Pore Detection in High-Resolution Fingerprint Images

With the emergence of high-resolution fingerprint sensors, there has been a lot of focus on level-3 fingerprint features, especially the pores, for the next generation automated fingerprint recognition systems (AFRS). Following the success of deep learning in various computer vision tasks, researchers have developed learning-based approaches for detection of pores in high-resolution fingerprint images. Generally, learning-based approaches provide better performance than hand-crafted feature-based approaches. However, domain adaptability of the existing learning-based pore detection methods has never been studied. In this paper, we study this aspect and propose an approach for pore detection in cross-sensor scenarios. For this purpose, we have generated an in-house 1000 dpi fingerprint dataset with ground truth pore coordinates (referred to as IITI-HRFP-GT), and evaluated the performance of the existing learning-based pore detection approaches. The core of the proposed approach for detection of pores in cross-sensor scenarios is DeepDomainPore, which is a residual learning-based convolutional neural network (CNN) trained for pore detection. The domain adaptability in DeepDomainPore is achieved by embedding a gradient reversal layer between the CNN and a domain classifier network. The proposed approach achieves state-of-the-art performance in a cross-sensor scenario involving public high-resolution fingerprint datasets with 88.12% true detection rate and 83.82% F-score.

High Resolution Fingerprint Retrieval Based on Pore Indexing and Graph Comparison

High Resolution Fingerprint Retrieval Based on Pore Indexing and Graph Comparison

Hierarchical Pore-Based High-Resolution Fingerprint Indexing

Pore-based fingerprint recognition has become more attractive in recent years because of its uniqueness and difficulty in forgeability. However, most of the existing methods use pore features for fingerprint verification rather than fingerprint indexing, and most of them use only local features of pores to evaluate the similarities between fingerprints. In this article, we present a hierarchical pore-based high-resolution fingerprint indexing system using two kinds of pore features (including pore-based global and local features). Our work consists of two major parts. First, we design a united model named deep pore-based global and local features model (DPGL) to extract pore-based global and local features of fingerprints simultaneously. The most important is the DPGL framework makes full use of pore information. It applies deconvolution module to learn a spatial attention mask that explicitly exploits self-supervision on pore locations. Second, we design a novel hierarchical pore-based fingerprint indexing approach. In the proposed approach, the reference fingerprints are primely selected based on pore-based global features. The fingerprints retained are selected based on pore-based local features subsequently. Finally, the candidate fingerprints are ranked according to pore information matching. Experiments on two datasets show that DPGL not only can accomplish the extraction of pore-based global and local features successfully, but also can achieve self-supervision on pore information. Compared with other state-of-the-art pore-based fingerprint indexing methods, the proposed approach reduces the pre-selection error rate by about 50% at penetration rate 1%, and performs at least 65% reduction in retrieval time.

Modeling and Performance Analysis of OAM-NFC Systems

Due to its low energy consumption and simplicity, near field communication (NFC) has been extensively used in various short-range transmission scenarios, for example, proximity payment and NFC entrance guard. However, the low data rate of NFC limits its application in high rate demanded scenarios, such as high-resolution fingerprint identification and streaming media transmission as well as the future promising high rate indoor communications among pads, phones, and laptops. In this paper, we model and analyze the performance of the orbital angular momentum based NFC (OAM-NFC) system, which can significantly increase the capacity of NFC. We first give the OAM system model. With coils circularly equipped at the transmitter and receiver, OAM-NFC signals can be transmitted, received, and detected. Then, we develop the OAM-NFC generation and detection schemes for NFC multiplexing transmission. We also analyze the OAM-NFC channel capacity and compare it with those of single-input-single-output (SISO) as well as multi-input-multi-output (MIMO) NFC. Simulation results validate the feasibility and capacity enhancement of our proposed OAM-NFC system. How different variables, such as the transceiver misalignment, the numbers of transceiver coils, and transceiver distance, impact the OAM-NFC capacity are also analyzed.

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.

Fingerprint pore matching using deep features

As a popular living fingerprint feature, sweat pore has been adopted to build robust high resolution automated fingerprint recognition systems (AFRSs). Pore matching is an important step in high resolution fingerprint recognition. This paper proposes a novel pore matching method with high recognition accuracy. The method mainly solves the pore representation problem in the state-of-the-art direct pore matching method. By making full use of the diversity and large quantities of sweat pores on fingerprints, deep convolutional networks are carefully designed to learn a deep feature (denoted as DeepPoreID) for each pore. The inter-class difference and intra-class similarity of pore patch pairs can be well solved using deep learning. The DeepPoreID is then used to describe the local feature for each pore and finally integrated into the classical direct pore matching method. More specifically, pore patches, which are cropped from both Query and Template fingerprint images, are imported into the well-trained networks to generate DeepPoreID for pore representation. The similarity between those DeepPoreIDs are then obtained by calculating the Euclidian Distance between them. Subsequently, one-to-many coarse pore correspondences are established via comparing their similarity. Finally, classical Weighted RANdom SAmple Consensus (WRANSAC) is employed to pick true pore correspondences from coarse ones. The experiments carried on the two public high resolution fingerprint database have shown the effectiveness of the proposed DeepPoreID, especially for fingerprint matching with small image size. Meanwhile, better recognition accuracy is achieved by the proposed method when compared with the existing state-of-the-art methods. About 35% rise in equal error rate (EER) and about 30% rise in FMR1000 when compared with the best result evaluated on the database with image size of 320 × 240 pixels.

Fast Pore Comparison for High Resolution Fingerprint Images Based on Multiple Co-Occurrence Descriptors and Local Topology Similarities

Pore-based fingerprint recognition has been researched for decades. Many algorithms have been proposed to improve the recognition accuracy of the system. However, the accuracies are always improved at the cost of speed. This article proposes a novel method to compare the pores in high-resolution fingerprint images using the popular coarse-to-fine strategy. A multiple spatial pairwise local co-occurrence descriptor is proposed to improve the calculation of the similarities between pores. It calculates multiple local co-occurrence statistics for each pore using its neighbors. The proposed method can establish correspondences between pores more accurately. The refinement of the correspondences is then achieved by using a local topology-preserving matching algorithm. The algorithm uses rotational invariant local structures and pore pair local topology similarities to calculate the cost of each correspondence. It can remove the mismatches more accurately and efficiently. The experimental results on two high-resolution fingerprint image databases show that the proposed algorithm perform well in both accuracy and speed comparing to the existing algorithms.

Fingerprint pores extraction by using automatic scale selection

Extraction of fingerprint sweat pores is a critical step in those applications which are based on highly secured features. Pores are varying in scale (size) and evenly distributed along the ridges. It is the main challenge to design a technique which determines the pores of different sizes in the fingerprint image. In this paper, pore extraction algorithm is proposed for high-resolution fingerprint images which utilised multiscale γ-normalised Laplacian of Gaussian (LoG) filter. A block-wise approach is implemented in which each region is filtered at multiple scale values. Scale space theory is applied and candidate pixels of high negative response are identified through local maxima approach. The efficacy of the proposed algorithm is tested by measuring average true detection rate (TDR) and average false detection rate (FDR). Results of the proposed algorithm achieve average TDR and average FDR values as 82.89% and 21.2% respectively which are better in comparison to the state-of-art techniques.

Fingerprint pores extraction by using automatic scale selection

Extraction of fingerprint sweat pores is a critical step in those applications which are based on highly secured features. Pores are varying in scale (size) and evenly distributed along the ridges. It is the main challenge to design a technique which determines the pores of different sizes in the fingerprint image. In this paper, pore extraction algorithm is proposed for high-resolution fingerprint images which utilised multiscale γ-normalised Laplacian of Gaussian (LoG) filter. A block-wise approach is implemented in which each region is filtered at multiple scale values. Scale space theory is applied and candidate pixels of high negative response are identified through local maxima approach. The efficacy of the proposed algorithm is tested by measuring average true detection rate (TDR) and average false detection rate (FDR). Results of the proposed algorithm achieve average TDR and average FDR values as 82.89% and 21.2% respectively which are better in comparison to the state-of-art techniques.

Fingerprint Pore Comparison Using Local Features and Spatial Relations

High-resolution fingerprint recognition has been a hot topic for many years. Compared with a traditional fingerprint image, a high-resolution fingerprint image can provide more features, such as pores and ridge contours. Introducing these features into fingerprint comparison and recognition can improve the recognition accuracy and reduce the risk of identification errors. This paper proposes a novel method for comparing pores on high-resolution fingerprint images. The method can be divided into two steps. In the first step, fingerprints are aligned using the pixel-category-distance-based data-driven descending algorithm. Traditionally, fingerprints are aligned based on feature points, such as minutiae and singular points. Such alignment methods are not suitable when dealing with partial fingerprints because small overlapping areas often do not contain enough features to guarantee a correct alignment. In this research, the ridges and valleys on fingerprints are used in combination with the orientation field for alignment. The proposed algorithm performs well when aligning both partial and full fingerprints. The common areas between the two images can be estimated based on the alignment result. In the second step, pores lying in the common areas are selected for comparison. To improve the comparison accuracy, pores are compared using local features and spatial relations. A graph comparison algorithm is designed in this step. The experimental results show that the proposed method is more accurate than other state-of-the-art pore comparison algorithms.

High resolution fingerprint recognition using pore and edge descriptors

Pores have been used for fingerprint recognition for many years. Introducing pores into fingerprint image comparison can improve the recognition accuracy. This paper proposes a novel method to compare pores on high resolution fingerprint images. The method consists of three steps. In the first step, pores on different images are clustered using a Root-SIFT descriptor. Then the pores in the same class will be compared using a rotational invariant edge descriptor and Hough transform. The descriptor consists of three parts: the length of the edge, the angles between the edge and the orientations of pores, and the angle between the orientations of pores. In the final step, one-to-one correspondences of pores are established based on the matched edges, and then refined using the spatial relations of the pores. To this end, a graph based refinement algorithm is designed. Experimental results on two high resolution fingerprint image databases show that the proposed algorithm is more accurate than other state-of-the-art pore comparison algorithms.

PoreNet: CNN-based Pore Descriptor for High-resolution Fingerprint Recognition

With the development of high-resolution fingerprint scanners, high-resolution fingerprint-based biometric recognition has received increasing attention in recent years. This paper presents a pore feature-based approach for biometric recognition. Our approach employs a convolutional neural network (CNN) model, DeepResPore, to detect pores in the input fingerprint image. Thereafter, a CNN-based descriptor is computed for a patch around each detected pore. Specifically, we have designed a residual learning-based CNN, referred to as PoreNet that learns distinctive feature representation from pore patches. For verification, the match score is generated by comparing pore descriptors obtained from a pair of fingerprint images in bi-directional manner using the Euclidean distance. The proposed approach for high-resolution fingerprint recognition achieves 2.91% and 0.57% equal error rates (EERs) on partial (DBI) and complete (DBII) fingerprints of the benchmark PolyU HRF dataset. Most importantly, it achieves lower FMR1000 and FMR10000 values than the current state-of-the-art approach on both the datasets.

Pore detection in high-resolution fingerprint images using deep residual network

This letter presents a residual learning-based convolutional neural network, referred to as DeepResPore, for detection of pores in high-resolution fingerprint images. Specifically, the proposed DeepResPore model generates a pore intensity map from the input fingerprint image. Subsequently, the local maxima filter is operated on the pore intensity map to identify the pore coordinates. The results of our experiments indicate that the proposed approach is effective in extracting pores with a true detection rate of 94:49% on Test set I and 93:78% on Test set II of the publicly available PolyU HRF dataset. Most importantly, the proposed approach achieves state-of-the-art performance on both test sets.

Pore-based indexing for fingerprints acquired using high-resolution sensors

With the advent of high-resolution fingerprint sensors, there have been efforts to develop high-resolution fingerprint identification systems. In this paper, we present an indexing method for high-resolution fingerprints using pore-based features. In the proposed approach, the dynamic pore filtering method is employed to extract pores from high-resolution fingerprint images. The extracted pores are treated as keypoints, and a pore descriptor is computed for each of the keypoints. The pore descriptor thus obtained is used as a feature vector for indexing. Finally, a cluster-based retrieval scheme is employed for fast and effective retrieval of the candidate list. Performance of the proposed approach has been evaluated on the Hong Kong PolyU high-resolution fingerprint databases, DBI and DBII and in-house databases, IITI-HRFP and IITI-HRF. The proposed indexing approach achieves an improvement of 67%, 49%, 42% and 28% points in pre-selection error rate (for penetration rate of 10%) over the existing method that employs pore features for indexing on DBI, DBII, IITI-HRFP and IITI-HRF, respectively. Most importantly, our approach provides better performance than the state-of-the-art minutiae-based fingerprint indexing algorithm on DBI and IITI-HRFP, which contain partial fingerprints.

Combined Application of Fourier-transform Infrared, Second Derivative Infrared, and Two-dimensional Correlation Infrared Spectroscopy to Monitor the Quality of Dendrobium Huoshanense During Processing

The objective of this study was to develop a multi-steps infrared macro-fingerprint method to discriminate Dendrobium huoshanense “Fengdou” (DHS FD) produced under different toasting and soaking conditions as well as to examine the effect of production conditions on the total polyphenol content, total water soluble polysaccharide content, and antioxidant activities. The results showed that though the Fourier transform infrared and second derivative infrared (SD-IR) spectra of the DHS FD processed under different conditions were similar visually, the DHS FDs could be discriminated by their characteristic peaks in the range of 1800–600 cm–1 from their IR and SD-IR files and the cross-peaks in the region of 1280–950 cm–1 in their two-dimensional correlation spectroscopy IR spectra. The total polyphenol content increased and the total water soluble polysaccharide content decreased in the DHS FDs toasted at temperatures from 110°C to 190°C for 30 min. Soaking process showed variable effects on the total polyphenol content and total water soluble polysaccharide content. The total polyphenol content of the DHS FDs toasted below 150°C increased as the soaking temperatures increased while the total water soluble polysaccharide content showed an opposite trend. Both the total polyphenol content and total water soluble polysaccharide content of the DHS FDs toasted at 170°C and 190°C showed little difference when soaking temperatures varied from 4°C to 100°C. The investigation of the 2,2-Diphenyl-1-picrylhydrazyl cleaving activity revealed that toasting and soaking process might increase the antioxidant activity of the DHS while too high toasting temperatures would decrease their activity. The multi-steps infrared macro-fingerprint analysis with high resolution and excellent macroscopic fingerprint features could effectively discriminate the DHS produced under different conditions without involving any separation and extraction. Consequently, the use of this method is recommended for quality control of the DHS FD.

Optimum Geometric Transformation and Bipartite Graph-Based Approach to Sweat Pore Matching for Biometric Identification

Sweat pores on the human fingertip have meaningful patterns that enable individual identification. Although conventional automatic fingerprint identification systems (AFIS) have mainly employed the minutiae features to match fingerprints, there has been minimal research that uses sweat pores to match fingerprints. Recently, high-resolution optical sensors and pore-based fingerprint systems have become available, which motivates research on pore analysis. However, most existing pore-based AFIS methods use the minutia-ridge information and image pixel distribution, which limit their applications. In this context, this paper presents a stable pore matching algorithm which effectively removes both the minutia-ridge and fingerprint-device dependencies. Experimental results show that the proposed pore matching algorithm is more accurate for general fingerprint images and robust under noisy conditions compared with existing methods. The proposed method can be used to improve the performance of AFIS combined with the conventional minutiae-based methods. Since sweat pores can also be observed using various systems, removing of the fingerprint-device dependency will make the pore-based AFIS useful for wide applications including forensic science, which matches the latent fingerprint to the fingerprint image in databases.