Fingerprint Identification Research Articles

Using the characteristic search algorithm in a library fingerprint identification system

As an important identification method, fingerprint recognition has a wide range of applications. To make the fingerprint recognition system of a library more efficient and secure, a recognition technology based on the characteristic search algorithm is proposed, and the performance of the algorithm is analysed. When a reasonable threshold is set, the matching error rate of the algorithm can be controlled at a lower level, and the algorithm can also ensure a higher fingerprint recognition accuracy and determine the overall accuracy. At the same time, three other identification algorithms of the same type are introduced: radio-frequency fingerprinting, convolutional neural network and local binary pattern. In a comparative experiment, it was found that the characteristic search algorithm model had the highest accuracy, with a value of 94.8%. When dealing with the same amount of fingerprint data, the recognition time of the algorithm model was the shortest. In addition, the area under the curve value corresponding to the receiver operating characteristic curve of the algorithm was the largest, and its value was 0.94. It is well known that the performance of the characteristic search algorithm is optimal and can effectively improve the operation efficiency of a library fingerprint identification system.

Multi-Layered Minutiae Extraction Based on Fusion-Attention for OCT Fingerprints

Minutiae extraction plays an important role in automated fingerprint identification systems. There are many minutiae extraction methods proposed for various fingerprints (rolled/slap/latent) in the past decades. However, for fingerprints by optical coherence tomography (OCT) imaging which have the essential property of multi-layered tissue structure, previous work may not be suitable. This paper is devoted to extract minutiae for OCT-based fingerprints based on the characteristics of OCT imaging and powerful deep neural networks. Specifically, we propose a multi-tissue-layered minutiae extraction network based on fusion-attention, involving a multi-tissue-layered feature extractor to fully explore the rich information in multiple subsurface fingerprints, and a one-stage detection framework to detect minutiae points. To demonstrate the effectiveness and robustness of the proposed method, a series of experiments have been carried out on the public OCT fingerprint benchmark dataset (https://github.com/FengLiu-0013/). Our method obtains the best results with an improvement of 8.3% in F1-score compared with state-of-the-art methods. The proposed method has also been evaluated for fingerprint identification using the extracted minutiae. Our method has achieved a matching error rate of 1.57%, with a relative reduction of 41% compared with state-of-the-art methods.

Unsupervised Radio Frequency Fingerprint Identification Based on Curriculum Learning

Radio frequency fingerprint (RFF) identification aims to identify emitters by extracting the inherent physical-layer features. Despite the significant accuracy achieved in a supervised learning manner, few works have focused on unsupervised RFF identification. To this end, we propose an unsupervised training paradigm based on the curriculum learning framework. Specifically, the unlabeled samples are first sorted by the signal-to-noise (SNR) of signals. Then, the network is pretrained with contrastive learning on the high SNR subset. To further improve identification performance, the model is gradually optimized from the easy (high SNR) to the hard (low SNR) subsets with pseudo label learning. Experimental results indicate that the proposed scheme achieves comparable performance against schemes based on supervised learning.

Field Performance and DNA Fingerprint Identification of New Egyptian Eggplant F1 Hybrids

Field Performance and DNA Fingerprint Identification of New Egyptian Eggplant F1 Hybrids

Direct Thermal Growth of Gold Nanopearls on 3D Interweaved Hydrophobic Fibers as Ultrasensitive Portable SERS Substrates for Clinical Applications.

Surface-enhanced Raman spectroscopy (SERS)-based biosensors have attracted much attention for their label-free detection, ultrahigh sensitivity, and unique molecular fingerprinting. In this study, a wafer-scale, ultrasensitive, highly uniform, paper-based, portable SERS detection platform featuring abundant and dense gold nanopearls with narrow gap distances, are prepared and deposited directly onto ultralow-surface-energy fluorosilane-modified cellulose fibers through simple thermal evaporation by delicately manipulating the atom diffusion behavior. The as-designed paper-based SERS substrate exhibits an extremely high Raman enhancement factor (3.9× 1011 ), detectability at sub-femtomolar concentrations (single-molecule level) and great signal reproductivity (relative standard deviation: 3.97%), even when operated with a portable 785-nm Raman spectrometer. This system is used for fingerprinting identification of 12 diverse analytes, including clinical medicines (cefazolin, chloramphenicol, levetiracetam, nicotine), pesticides (thiram, paraquat, carbaryl, chlorpyrifos), environmental carcinogens (benzo[a]pyrene, benzo[g,h,i]perylene), and illegal drugs (methamphetamine, mephedrone). The lowest detection concentrations reach the sub-ppb level, highlighted by a low of 16.2 ppq for nicotine. This system appears suitable for clinical applications in, for example, i) therapeutic drug monitoring for individualized medication adjustment and ii) ultra-early diagnosis for pesticide intoxication. Accordingly, such scalable, portable and ultrasensitive fibrous SERS substrates open up new opportunities for practical on-site detection in biofluid analysis, point-of-care diagnostics and precision medicine.

A Stress Test for Robustness of Photo Response Nonuniformity (Camera Sensor Fingerprint) Identification on Smartphones

In the field of forensic imaging, it is important to be able to extract a camera fingerprint from one or a small set of images known to have been taken by the same camera (or image sensor). Note that we are using the word fingerprint because it is a piece of information extracted from images that can be used to identify an individual source camera. This technique is very important for certain security and digital forensic situations. Camera fingerprint is based on a certain kind of random noise present in all image sensors that is due to manufacturing imperfections and is, thus, unique and impossible to avoid. Photo response nonuniformity (PRNU) has become the most widely used method for source camera identification (SCI). In this paper, a set of attacks is designed and applied to a PRNU-based SCI system, and the success of each method is systematically assessed both in the case of still images and in the case of video. An attack method is defined as any processing that minimally alters image quality and is designed to fool PRNU detectors or, in general, any camera fingerprint detector. The success of an attack is assessed as the increment in the error rate of the SCI system. The PRNU-based SCI system was taken from an outstanding reference that is publicly available. Among the results of this work, the following are remarkable: the use of a systematic and extensive procedure to test SCI methods, very thorough testing of PRNU with more than 2000 test images, and the finding of some very effective attacks on PRNU-based SCI.

MOF-gold core-satellite nanostructure based SERS platform for fentanyl detection in multiple complex samples

As one of the potent synthetic opioids, fentanyl is liable to be adulterated with illicit drugs, which raises the risk of drug overdose and has emerged as a potential threat to public health. In field testing, rapid identification of fentanyl from adulterated or complex biological samples is critical. Given the advantages of Raman spectroscopy in fingerprint identification and the portability of the equipment, it is undoubtedly an excellent alternative. Here, we designed and fabricated a novel SERS substrate by amino-triggered growth of AuNPs satellite surrounding fentanyl-affiliative NH2-MIL-101(Fe). The obtained NH2-MIL-101 @AuNPs play a dual role, i) fentanyl adsorption; ii) hot spots enhanced Raman signals. With the aid of NaI, a highly sensitive and specific detection of fentanyl was achieved. Furthermore, fentanyl-d5 was used as an internal standard to calibrate the signal of fentanyl for a more reliable determination. The integrated NH2-MIL-101 @AuNPs platform was further explored for the quantitative detection of fentanyl in complex biological samples. Notably, the background interference of plasma was effectively eliminated by the addition of BSA. This integrative approach has the potential to be applied to other framework materials for sensing and detection of various analytes, which opens up a more comprehensive range of applications for MOFs.

An Innovative Possibilistic Fingerprint Quality Assessment (PFQA) Filter to Improve the Recognition Rate of a Level-2 AFIS.

In this paper, we propose an innovative approach to improve the performance of an Automatic Fingerprint Identification System (AFIS). The method is based on the design of a Possibilistic Fingerprint Quality Assessment (PFQA) filter where ground truths of fingerprint images of effective and ineffective quality are built by learning. The first approach, QS_I, is based on the AFIS decision for the image without considering its paired image to decide its effectiveness or ineffectiveness. The second approach, QS_PI, is based on the AFIS decision when considering the pair (effective image, ineffective image). The two ground truths (effective/ineffective) are used to design the PFQA filter. PFQA discards the images for which the AFIS does not generate a correct decision. The proposed intervention does not affect how the AFIS works but ensures a selection of the input images, recognizing the most suitable ones to reach the AFIS's highest recognition rate (RR). The performance of PFQA is evaluated on two experimental databases using two conventional AFIS, and a comparison is made with four current fingerprint image quality assessment (IQA) methods. The results show that an AFIS using PFQA can improve its RR by roughly 10% over an AFIS not using an IQA method. However, compared to other fingerprint IQA methods using the same AFIS, the RR improvement is more modest, in a 5-6% range.

Implications of Vascularized Composite Allotransplantation in Plastic Surgery on Legal Medicine.

When a patient receives a transplant-be it classically an organ or bone marrow or, more recently, composite allotransplantations of the limb or face-it can result in artificial chimerism. Such chimerism raises considerations in forensic medicine, a field that relies on the collection and identification of biological samples from crime scenes. Beyond this chimerism, composite allotransplantations create further challenges. After screening the literature and press releases, we provide a brief history and summary of some of the technologies used in forensic identification, explaining their advantages and pitfalls in the light of transplantation and cautioning against misidentifying those who evade justice by taking advantage of such considerations. With face transplantation, patients can receive the skin, hair, salivary glands, teeth, and oral and nasal mucosa of their donors, components which hold great importance in forensic science. Modern technologies such as computer-assisted facial recognition, although gradually becoming more accurate over time, also face new challenges in this post-transplantation era as facial recognition software can be misled by surgical alterations of the face or face transplantation. With limb transplantation, there is an impact on fingerprint identification. Both surgical transplantation techniques and forensic technologies have seen incomprehensibly great innovation in the past century. Given the growing rate of successful composite transplantation in the USA and worldwide, it is now important for law enforcement agents to be aware of the new possibility of having two sets of genetic material, hair, saliva, fingerprints, or even facial recognition data for the same individual.

Smart Multi Verification Based Security System

In this technologically evolving era, security plays a significant role in preventing different assets and crimes. This inconsistency developed an innovative idea to improve the level and solve the existing problems. This paper proposes a well-suited multilayer security system for homes, bank lockers, and more locations we can use it. Traditionally, password and biometric double-layer security systems use everywhere, but this embedded solution combines RFID, OTP, and fingerprint identification in sequence. Essential modules are connected and controlled through a microcontroller with a GSM module. Every operation done by the system pushes to the IoT cloud, and the mobile application shows the status of every action. The right authorized access can let the magnetic switch open, and every unauthorized access turns on the alarm with appropriate message notifications. The Proposed system is more effective and reliable due to multistage security, and it is not easy to crack with the combination of all three stages. The whole system's workings indicate by led flashes. This implementation has shown better results and a higher performance rate than existing methods

Comprehensive Identification of Chemical Fingerprint and Screening of Potential Quality Markers of Aloe vera (L.) Burm. f. from Different Geographical Origins via Ultra-High-Performance Liquid Chromatography Hyphenated with Quadrupole–Orbitrap-High-Resolution Mass Spectrometry Combined with Chemometrics

An integrated strategy was developed for the systematic chemical fingerprint and chemometrics analysis for the quality assessment of Aloe vera (L.) Burm. f. The ultra-performance liquid chromatography fingerprint was established, and all common peaks were tentatively identified by using ultra-high-performance liquid chromatography hyphenated with quadrupole-orbitrap-high-resolution mass spectrometry. Afterwards, the datasets of common peaks were subjected to hierarchical cluster analysis, principal component analysis and partial least squares discriminant analysis to holistically compare the differences. The results revealed that the samples were predicted to fall into four clusters, which were related to four different geographical locations. Using the proposed strategy, aloesin, aloin A, aloin B, aloeresin D and 7-O-methylaloeresin A were rapidly determined to be the potential characteristic quality markers. Finally, five screened compounds in 20 batches of samples were simultaneously quantified, and their total contents were ranked as follows: Sichuan province > Hainan province > Guangdong province > Guangxi province, which suggests that geographical origins may be an important factor affecting the quality of A. vera (L.) Burm. f. This new strategy can not only be used to explore possibly the latent active substance candidates for pharmacodynamic studies, but it is also an efficient analytical strategy for other complex traditional Chinese medicine systems.

Metabolite Fingerprinting for Identification of Panax ginseng Metabolites Using Internal Extractive Electrospray Ionization Mass Spectrometry.

Ginseng, a kind of functional food and medicine with high nutritional value, contains various pharmacological metabolites that influence human metabolic functions. Therefore, it is very important to analyze the composition and metabolites of ginseng. However, the analysis of active metabolites in ginseng samples usually involves various experimental steps, such as extraction, chromatographic separation, and characterization, which may be time-consuming and laborious. In this study, an internal extractive electrospray ionization mass spectrometry (iEESI-MS) method was developed to analyze active metabolites in ginseng samples with sequential sampling and no pretreatment. A total of 44 metabolites, with 32 ginsenosides, 6 sugars, and 6 organic acids, were identified in the ginseng samples. The orthogonal partial least-squares discriminant analysis (OPLS-DA) score plot showed a clear separation of ginseng samples from different origins, indicating that metabolic changes occurred under different growing conditions. This study demonstrated that different cultivation conditions of ginseng can be successfully discriminated when using iEESI-MS-based metabolite fingerprints, which provide an alternative solution for the quality identification of plant drugs.

Novel development of zinc oxide–coated carbon nanoparticles from pineapple leaves using sol gel method for optimal adsorption of Cu2+ and reuse in latent fingerprint application

This study underlines a latest approach of preparing nitrogen carbon nanoparticles fused on zinc oxide nanoparticle nanocomposite (N-CNPs/ZnONP nanocomposite) for the uptake of copper ions (Cu2+) from wastewater using a sol gel method. The metal loaded adsorbent was then applied in the latent fingerprint application. N-CNPs/ZnONP nanocomposite proved to be a good sorbent for the optimal adsorption of Cu2+ at pH 8 and 1.0 g/L dosage. Langmuir isotherm best fitted the process with the maximum adsorption capacity of 285.71 mg/g that was superior to most values reported in other studies for the removal of Cu2+. At 25 °C, the adsorption was spontaneous and endothermic. Furthermore, Cu2+-N-CNPs/ZnONP nanocomposite revealed to be sensitive and selective for latent fingerprint (LFP) identification on a variety of porous surfaces. As a result, it is an excellent identifying chemical for latent fingerprint recognition in forensic science.Graphical

DESIGN AND DEVELOPMENT OF A HOSPITAL BIOMETRIC DATA MANAGEMENT SYSTEM

The need to drastically reduce the delay faced by patients before they receive treatment in the University Clinic and the need to reduce the occurrence of medical mistakes due to patient’s misidentification led to this work which aimed at developing a hospital biometric data management system.Java was the programming language of choice and Netbeans was the Integrated Development Environment (IDE) used to design the Graphical User Interphase (GUI) and write codes. For the backend database, MySQL was used for demonstration purposes. The analytical diagrams were drawn using VioletUMLEdit. The Griaule Fingerprint Software Development Kit (SDK) was used to implement the fingerprint capture, enrollment, identification and verification features of the software.The tests and results obtained showed that with this hospital management system, the time taken to retrieve patient’s record has been reduced from several minutes to less than five seconds thereby eliminating the delay experienced by patients during search of their records. The problem of medical mistakes due to patient’s misidentification was also addressed by the biometric feature of the management system.

Slice combination convolutional neural network based radio frequency fingerprint identification for Internet of Things

Slice combination convolutional neural network based radio frequency fingerprint identification for Internet of Things

Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet.

In power fingerprint identification, feature information is insufficient when using a single feature to identify equipment, and small load data of specific customers, difficult to meet the refined equipment classification needs. A power fingerprint identification based on the improved voltage-current(V-I) trajectory with color encoding and transferred CBAM-ResNet34 is proposed. First, the current, instantaneous power, and trajectory momentum information are added to the original V-I trajectory image using color coding to obtain a color V-I trajectory image. Then, the ResNet34 model was pre-trained using the ImageNet dataset and a new fully-connected layer meeting the device classification goal was used to replace the fully-connected layer of ResNet34. The Convolutional Block Attention Module (CBAM) was added to each residual structure module of ResNet34. Finally, Class-Balanced (CB) loss is introduced to reweight the Softmax cross-entropy (SM-CE) loss function to solve the problem of data imbalance in V-I trajectory identification. All parameters are retrained to extract features from the color V-I trajectory images for device classification. The experimental results on the imbalanced PLAID dataset verify that the method in this paper has better classification capability in small sample imbalanced datasets. The experimental results show that the method effectively improves the identification accuracy by 4.4% and reduces the training time of the model by 14 minutes compared with the existing methods, which meets the accuracy requirements of fine-grained power fingerprint identification.

Suppressed Self‐Reduction of Manganese in Mg2SnO4 via Li+ Incorporation with Polychromatic Luminescence for Versatile Applications

AbstractHerein, the self‐reduction behavior of manganese (Mn) in Mg2SnO4 (MSO) host lattices prepared by using the solid‐state reaction technique in air atmosphere is reported. Excited by 282 nm, only the emission of Mn2+ is seen, and the observed results are confirmed by theoretical calculation based on the density functional theory. Noteworthy, with the codoping of Li+, the self‐reduction process of Mn is suppressed, leading to the coexistence of Mn2+ and Mn4+ in MSO host lattices along with the multicolor emissions. For the suppressed self‐reduction behavior, possible hypotheses are proposed to elucidate the corresponding mechanisms. Furthermore, benefiting from the different thermal quenching behaviors of Mn2+ and Mn4+, ratiometric optical thermometers with high sensitivity of 6.59% K−1 are designed. Moreover, the prepared phosphors possess a good green afterglow phenomenon, in which their trap depth are 0.71 and 0.91 eV. In addition, via using the designed phosphors, applications in anti‐counterfeiting, fingerprint identification, and optical information storage are also realized.

Ultra-sensitive fluorescent and colorimetric probes for femtomolar detection of picric acid: Mechanochromic, latent fingerprinting, and pH responsive character with AIE properties

• Probes 1(a-c) were synthesized through one-pot multicomponent reaction. • This study offers design strategy to transform ACQ into AIE feature. • Probes showed mechanochromism, pH responsive, and identification of latent fingerprints. • Probes displayed ultrasensitive femtomolar level sensing of picric acid (PA) • Selective sensing of PA was achieved through the predominant involvement of ground state complexation. Multiple stimuli responsive easy to synthesize organic probes with remarkable structural properties are imminently essential. To this endeavor, 1,4-dihydropyridine (DHP) based three new probes 1(a-c) were successfully synthesized and characterized through modern spectroscopic and mass spectrometry techniques. Planar dihydropyridine was appended with multiple donor-acceptor and rotors to bring aggregation induced emission (AIE) character in probes 1(a-b) . Interestingly, probe 1c displayed aggregation caused quenching (ACQ) effect due to perpendicularly attached naphthyl ring. Therefore, this study presents extensive structure-property relationship to invoke AIE character. Moreover, probes 1(a-c) exhibited high photostability, reversible mechanochromism, pH responsive character, and adequate identification of latent fingerprints (LFPs). Importantly, multifunctional probes 1(a-c) served as highly selective fluorescent and colorimetric probes for picric acid (PA). In solution, highly sensitive femtomolar (fM) level (limit of detection (LOD); 12.3, 14.6, and 27.1 fM) detection of PA was attributed to combined effect of ground-state charge transfer complex formation and Forster resonance energy transfer (FRET). Favorable complexation between probes and PA was proved through UV-visible absorption, density functional theory (DFT), and dynamic light scattering (DLS) studies. Advantageously, probes were able to detect picric acid in vapor phase. In addition, probes were practically applicable for the contact mode detection of PA and sensing of PA in LFPs and industrial samples.

RF Fingerprinting Identification Based on Spiking Neural Network for LEO–MIMO Systems

Low earth orbit (LEO) satellites promise to be a source of innovation in wireless communication and their physical layer security deserves attention. However, many physical layer security techniques are hampered by excessive power consumption and high latency when used for LEO satellites. This letter proposes an energy-efficiency radio frequency fingerprinting identification (RFFI) method for LEO satellite communication systems based on spiking neural networks (SNN). In addition, we investigate a channel-independent RFF feature transformation and data augmentation to enhance system robustness. Numerical results show that our proposed model can yield identification accuracy up to 95.26% at a signal-to-noise ratio (SNR) of 25dB on orthogonal frequency division multiplexing (OFDM) signals, and reduces power consumption by 63.3% compared to existing models of comparable accuracy on FPGA.

Identification of substrate-specific allosteric fingerprints in matrix metalloprotease-1 at the single molecule level for precision control of function.

MMPs, a family of 23 human enzymes, interact with and degrade many biomolecules. Any drug targeting the active site of MMPs inhibits intended and unintended functions and results in adverse side effects. If we identify substrate-specific allosteric locations (distant from the active site), we may change one role of MMPs without affecting their other functions for fewer side effects. We attached fluorescent dyes at two specific sites on the two domains of MMP1 to measure inter-domain dynamics on four water-insoluble substrates: collagen fibril, fibrin, alpha-synuclein (aSyn) aggregates, and amyloid-beta (AB) aggregates. We calculated the distance between the two locations from the single molecule Forster Resonance Energy Transfer (smFRET) between the two dyes, measured using a Total Internal Reflection Fluorescence (TIRF) microscope. Low FRET (open) MMP1 conformations with well-separated domains are relevant for collagen, fibrin, and aSyn. In contrast, high FRET (closed) conformations apply to AB aggregates. Functionally relevant conformations are present in active MMP1 but significantly absent in catalytically inactive MMP1. We found that each substrate has unique fingerprints (residues) in MMP1 and leads to changes at the catalytic and distant allosteric sites. Screening drugs against substrate-specific allosteric sites predicted substrate-specific drugs for targeting MMP1 based on single molecule insights.