Fingerprint Identification Method Research Articles

Radio frequency fingerprint recognition method based on prior information

The open wireless communication environment is vulnerable to various malicious attacks. Wireless communication hardware devices have unique physical layer characteristics. As an inherent unique feature of wireless signals, radio frequency fingerprints provide a guarantee for the identification and verification of wireless signals. Most of the existing radio frequency fingerprint identification methods only extract fingerprints from one of the steady-state signals or transient signals. Neglecting the connection between the two wireless communication signals results in low identification accuracy of the radio frequency fingerprint identification method under the condition of a low signal-to-noise ratio. Aiming at the respective characteristics of these two signals, a radio frequency fingerprinting method combining transient and steady-state signals based on prior information of wireless signals is proposed. This method combines the characteristic stability of steady-state signals and the integrity characteristics of transient signals, which can effectively identify and classify wireless signals and achieve excellent recognition under low signal-to-noise ratio conditions. The effectiveness of the proposed method is verified by experimental comparison with the traditional radio frequency fingerprinting method on the LFM signal dataset.

Fingerprint Identification Method Based on Convulsional Neural Networks

The article presents an advanced method of fingerprint identification based on convolutional neural network (CNN) technology. This work elaborately describes the development and implementation process of a specialized CNN architecture for detecting and verifying the authenticity of fingerprints. Utilizing the comprehensive Socofing dataset allowed for an in -depth analysis of the model’s ability to distinguish between genuine and fabricated fingerprints, where the model demonstrated impressive accuracy – up to 98.964%. Special attention is given to error analysis, including the false discovery and omission rates, pointing towards potential directions for further improvement. Besides highlighting the technical aspects and high identification accuracy, the article also addresses potential challenges and limitations that the method might encounter. This includes issues related to the imbalance and diversity of data in the Socofing set, as well as limitations associated with computational resources when training deep neural networks. Potential pathways for model optimization are discussed, particularly focusing on reducing the false omission rate, which could improve user experience in authentication. The concluding section of the article emphasizes the importance of the presented work for the security sector, where precise authentication of fingerprint images is critically needed. The obtained results can be considered a solid foundation for future scientific developments in this direction. Additionally, the need for systematic updates and modifications of the model is highlighted to adapt it to continually improved imitation techniques, ensuring its long-term relevance and effectiveness.

Establishment of XRD fourier fingerprint identification method of realgar decoction pieces and its anti-tumor activity in tumor-in-situ transplanted mice

Ethnopharmacological relevanceRealgar, a traditional mineral Chinese medicine, has been used in China for more than 2000 years. It has been recorded in many ancient and modern works that it has anti-cancer and anti-tumor effects. Of course, colon cancer is also within the scope of its treatment. Realgar needs to be processed into realgar decoction pieces by water grinding before being used for medicine. To ensure the consistency of efficacy and quality of realgar decoction pieces, modern methods need to be used for further quality control. Aim of the studyThe research of traditional mineral Chinese medicine is relatively difficult, and the related research is less. The purpose of this study is to control the quality of realgar decoction pieces by modern analytical technology and analyze its components. On this basis, its anti-colon cancer activity was discussed. Materials and methodsSeveral batches of realgar decoction pieces were analyzed by XRD, and the components of realgar decoction pieces were obtained. The quality control fingerprints of realgar decoction pieces were established by processing XRD spectra and similarity evaluation. Then, the effects of realgar decoction pieces on apoptosis of CT26 and HTC-116 cells were observed in vitro by Hoechst 33258 staining, flow cytometry, measurement of mitochondrial membrane potential and Western blot; In vivo, the mouse model of tumor-in-situ transplantation of colon cancer was established, and the related indexes were observed. ResultThe explorations showed that the XRD Fourier fingerprints of realgar decoction pieces samples that had the same phase revealed 10 common peaks, respectively. The similarity evaluation of the established XRD Fourier fingerprint was greater than 0.900. We also demonstrated that realgar decoction pieces can promote apoptosis and inhibit tumor growth in colon cancer cells, its activating effect on p53 protein, and its safety when used within reasonable limits. ConclusionThe quality control of realgar decoction pieces by XRD is scientific and has the inhibitory effect on colon cancer, which has the development potential.

Reservoir Desilting and Treatment of Silted Sediment

This paper aims to explore the significance and methods of sediment treatment in reservoirs. It first analyzes the impact of sediment on protective engineering, highlighting how sediment reduction exacerbates soil erosion and riverbed siltation, directly threatening the safety of protective engineering. Next, it introduces the application of compound fingerprint identification method in sediment source analysis, which accurately identifies sediment sources by comprehensively considering multiple characteristics, providing crucial scientific basis for protective engineering. The paper then discusses three main sediment treatment methods: mechanical dredging technology, sedimentation tank treatment, and biological treatment. Mechanical dredging technology is simple and efficient but suffers from issues like noise and high maintenance costs. Sedimentation tank treatment effectively improves water quality, while biological treatment technology has recently gained significant attention for purifying water bodies using microorganisms and plants, enhancing ecological quality. In conclusion, the paper emphasizes the importance of selecting suitable treatment methods based on specific conditions to achieve maximum efficiency and cost-effectiveness.

Authenticity and adulteration identification of Bubali Cornu based on DNA fingerprints

Whether adulteration exists is a difficult problem in the identification of traditional Chinese medicine(TCM). Bubali Cornu is mainly available in the medicinal material market in the form of buffalo horn silk or buffalo horn powder but lacks obvious identification characteristics, so there is a risk of adulteration. However, the method of identification of adulteration in Bubali Cornu is lacking at present. In order to ensure authenticity and identify adulteration of TCM Bubali Cornu, control the quality of TCM Bubali Cornu, and ensure the authenticity of clinical use, the DNA fingerprints of 43 batches of samples from pharmaceutical companies and medicinal material markets were identified, and the amplification primers of fluorescent DNA fingerprints of Bubali Cornu and Bovis Grunniens Cornu were screened. The DNA fingerprints of Bubali Cornu were obtained by fluorescent capillary typing. The identification effect of fluorescent capillary typing on different adulteration ratios was also tested. Two pairs of fluorescent STR typing primers, namely 16Sa and CRc, which can distinguish Bubali Cornu and Bovis Grunniens Cornu, were screened out, and a DNA fingerprint identification method was established. The 16Sa migration peaks of Bovis Grunniens Cornu and Bubali Cornu were 223.4-223.9 bp and 225.5-226.1 bp. The CRc migration peaks of Bovis Grunniens Cornu and Bubali Cornu were 518.8-524.8 bp and 535.9-542.5 bp. The peak height of the migration peak could be used for preliminary quantification of the adulterants with an adulteration ratio below 50%, and the quantitative results were similar to the adulteration ratio. In this study, a simple and quick universal DNA fingerprint method was established for the identification of Bubali Cornu and its adulterants, which could realize the identification of TCM Bubali Cornu and the semi-quantitative identification of the adulterants.

TEXTURE BASED FINGER PRINT IDENTIFICATION

In recent days , many digital finger print image based identification applications are used widely for human identity . Finger print features like minutiae (ridge lines, bifurcation, ridge ending) delta point, core point, texture patterns of ridges and valleys etc., are used for finger print identification. Reliable finger print identification system uses the finger print features to provide higher accuracy . Many finger print identification systems have been proposed based on minutiae , ridge lines. But these systems performance are not effective as compared to texture based methods. In this article , significance of feature extraction techniques , various texture based finger print identification methods are investigated and analysed . Limitations of minutiae, , and based identification systems are discussed. Also how effective the texture based identification systems are discussed. A comparative analysis of experimental results of various methods of finger print identification is done. This paper is concluded with the finding of appropriate method of texture based feature extraction for effective identity authentication. Keywords : Finger print feature, Texture feature, feature extraction, ridges, finger print recognition.

Data-and-Knowledge Dual-Driven Radio Frequency Fingerprint Identification

Wireless network security can be improved by radio frequency fingerprinting due to its stability and uniqueness. Although many radio frequency fingerprint identification methods based on deep learning have been proposed, they have low identification accuracy, especially at low signal-to-noise ratio. In order to overcome this drawback, a data-and-knowledge dual-driven radio frequency fingerprint identification scheme is proposed by utilizing a knowledge-driven multi-scale attention convolutional network. The protocol knowledge is exploited to provide more advanced semantics. Moreover, the multi-scale attention convolutional network is utilized to capture higher-level features. Simulation results demonstrate that our designed scheme has the best performance than the representative schemes in the matter of convergence speed and identification accuracy.

Multilevel Fine Fingerprint Authentication Method for Key Operating Equipment Identification in Cyber-Physical Systems

The key equipment in a cyber-physical system (CPS) can be changed or replaced by untrusted equipment, which causes the system to make incorrect decisions, which threatens people's lives and property security. Inspired by the identification of human genes, this research establishes a data fingerprint identification method for equipment. This method provides a security guarantee for the operation of a CPS. Thus, this research makes three contributions. First, it is the first attempt to represent device data information with a mathematical representation model based on multiorder terms. Second, we originally attempt to establish a multigranularity feature extraction method. Then, for the first time, we try to propose different scale matching methods for different granularity features. In addition, an F-404 aircraft example is used to verify the new method. The experimental results show that stealthy false data attacks can bypass the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\chi ^{2}}$</tex-math></inline-formula> detector and destroy the operation of key equipment. However, the method proposed in this article can detect stealthy false data attacks and alert managers so that they can deal with the attacks in time; hence, the method proposed in this article is better than the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\chi ^{2}}$</tex-math></inline-formula> detector. Similarly, we contrast the proposed method with recent competitor schemes and provide tangible evidence of the effectiveness of the proposed solution.

A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes

Fingerprints are the most widely used of all biological characteristics in public safety and forensic identification. However, fingerprint images extracted from the crime scene are incomplete. On the one hand, due to the lack of effective area in partial fingerprint images, the extracted features are insufficient. On the other hand, a broken ridge may lead to a large number of false feature points, which affect the accuracy of fingerprint recognition. Existing fingerprint identification methods are not ideal for partial fingerprint identification. To overcome these problems, this paper proposes an attention-based partial fingerprint identification model named APFI. Firstly, the algorithm utilizes the residual network (ResNet) for feature descriptor extraction, which generates a representation of spatial information on fingerprint expression. Secondly, the channel attention module is inserted into the proposed model to obtain more accurate fingerprint feature information from the residual block. Then, to improve the identification accuracy of partial fingerprints, the angular distance between features is used to calculate the similarity of fingerprints. Finally, the proposed model is trained and validated on a home-made partial fingerprint image dataset. Experiments on the home-made fingerprint datasets and the NIST-SD4 datasets show that the partial fingerprint identification method proposed in this paper has higher identification accuracy than other state-of-the-art methods.

FUID SEVM Fingerprint Unique ID Smart Electronic Voting Machine

Abstract: Secure voting is ensured by the Electronic Voting Machine. People should believe that their vote is secured and there are no unfair practices. The main objective of this project is to develop a secure Electronic Voting Machine using fingerprint identification method, we are using system database for fingerprint access. At the time of voting in elections, the verification of the electronic voting process can be done by scanning the veins on the fingers which allows voting to be reset so voters can cast their vote. We are also giving a barcode scan feature where user will scan to authenticate himself. System then show the list of candidate user, User will vote by pressing the button and all the data will be store in storage device. Fingerprint scanning is used to provide security to prevent fake, duplicate voting etc. It also increases the accuracy and speed of the process. The purpose of such a system is to ensure that only the legitimate user and no one else has access to the voting rights. If a particular pattern matches anyone in the available record, voting access is granted. But in case the pattern does not match the records in the database, or in the case of repetition, access to cast a vote is denied or the vote is rejected.

Optimized LightGBM Power Fingerprint Identification Based on Entropy Features.

The huge amount of power fingerprint data often has the problem of unbalanced categories and is difficult to upload by the limited data transmission rate for IoT communications. An optimized LightGBM power fingerprint extraction and identification method based on entropy features is proposed. First, the voltage and current signals were extracted on the basis of the time-domain features and V-I trajectory features, and a 56-dimensional original feature set containing six entropy features was constructed. Then, the Boruta algorithm with a light gradient boosting machine (LightGBM) as the base learner was used for feature selection of the original feature set, and a 23-dimensional optimal feature subset containing five entropy features was determined. Finally, the Optuna algorithm was used to optimize the hyperparameters of the LightGBM classifier. The classification performance of the power fingerprint identification model on imbalanced datasets was further improved by improving the loss function of the LightGBM model. The experimental results prove that the method can effectively reduce the computational complexity of feature extraction and reduce the amount of power fingerprint data transmission. It meets the recognition accuracy and efficiency requirements of a massive power fingerprint identification system.

Adapting Fingerprints Within the NSW Police Force: An Historical Examination of the Geographical Barriers and Implications for Rural and Regional Policing

In the 1890s and 1900s, societies in Western nations, and in particular densely populated cities like London and Paris, became overtly concerned with the need to identify ‘habitual criminals’. In response, in 1903, NSW became the first Australian state to develop a designated fingerprint section or bureau within their police force, which, following international trends, was very much focused on metropolitan NSW. According to Cauchi and Knepper (2009), fingerprint technology as a form of criminal identification “was the mark of cosmopolitan police organization” (p. 74). Criminal identification was principally identified as a problem in the rapidly growing cities, and thus, fingerprinting resources were directed to those areas. As a result, for the first few decades of use, fingerprint identification in rural and regional areas was limited. For example, only officers in metropolitan NSW were trained in fingerprint identification and comparison methods, and police around regional NSW were expected to take finger-print exhibits to metropolitan areas for analysis. The limitations in technology available during the initial period of fingerprinting resulted in regional NSW police facing more barriers to the use of fingerprint identification technology than their metropolitan counterparts. To understand these barriers and the limitations of fingerprinting in rural areas, this paper explores the initial introduction of fingerprinting into the NSW Police system from 1903-1930, focusing upon the challenges and barriers experienced by regional police agencies in using what is now one of the most common forensic identification tools available to police worldwide.

Innovation Research on Fingerprint and DNA Identifications

In this study, two models for fingerprint and DNA identifications are constructed based on modern technologies, while offering significant advances over prior models. Our models have high credibility, obtaining relatively accurate results under different circumstances. Under different assumptions, this model tests the probability of the validity in the statement that human fingerprints are unique to be 93.94%. In other words, the percentage of misidentification is 6.06%. This model is a robust fingerprint identification method that can tolerate highly nonlinear deformations. The model is tested on the basis of a self-built database, proving that the model has high credibility, and convincing results are obtained from sensitivity analysis. In order to estimate the odds of misidentification by DNA evidence, we emphasized on two factors that might contribute to misidentification: random match probability and the probability arising from laboratory errors. Then, a model is developed using Bayes’ theorem to reveal the inherent relationship between them, which carries some reference value. The probability of matching by DNA evidence is estimated based on the changes in the significant level. Finally, the probabilities of misidentification by both fingerprint evidence and DNA evidence are compared using numerous data. We found that the probability of the former is 6.06% and that of the latter is smaller than 4.0 x 10?10. Therefore, it can be concluded that DNA identification is far better than that of fingerprint identification.

Mitigating MasterPrint vulnerability by employing minutiae geometry

The biometric system accepts a fingerprint of an individual to authenticate, authorize, or identify a person. However, some devices with a small sensor use a partial fingerprint as input. Recent research has shown that partial fingerprints are not unique compared to the full fingerprint, leading to the MasterPrint vulnerability. A MasterPrint is a partial fingerprint identifying at least 4% distinct subjects enrolled with the database. An adversary exploits such MasterPrints for executing financial frauds, mounting a presentation attack, or criminal activities. Our work proposes minutiae geometry-based partial fingerprint identification method targeted to alleviate the MasterPrint vulnerability. The experiments comprise partial datasets created from five full-fingerprint benchmark datasets and six existing approaches to compare the results of the proposed method. The observations show that the proposed approach achieved up to 97% identification accuracy while generating merely 0.1% MasterPrints.

Multi-information fingerprint identification method based on interactive genetic algorithm

In order to overcome the shortcomings of traditional fingerprint identification methods, such as low computational efficiency and high false classification rate, a new multi-information fingerprint identification method based on interactive genetic algorithm is proposed. Firstly, the multi-information fingerprint image is pre-processed to extract the feature points. Then, combined with the interactive genetic algorithm, the rotation angle of the two fingerprint images is determined. At the same time, the fingerprint offset code is coded, and the matching setting function is set. The matching degree of the two fingerprints is determined by the interactive genetic algorithm, which effectively realises the multi-information fingerprint identification. Finally, the simulation experiment is carried out. The experimental results show that the proposed method can effectively reduce the false classification rate, reduce the average matching time of fingerprint image, and improve the operation efficiency. The minimum error rate is only 1.02%.

IL SEGNALAMENTO DEL DELINQUENTE BY SALVATORE OTTOLENGHI: THE STUDY OF DERMATOGLYPHICS IN ANTHROPOMETRIC CABINETS

The dermatoglyphics are signs of the human variety, as they are absolutely different from one human being to another. For this reason, and for their characteristics of uniqueness, classification, and inalterability, the papillary ridges on the fingertips represent elements of a sure differentiation between one person and another. Fingerprints are, therefore, very helpful in identifying a human being. Salvatore Ottolenghi was the first to utilize the fingerprinting system to identify individuals, and he introduced this system in his “Cartellino di riconoscimento (identification card)” in 1902. He was sure about the scientific validity of this method, which he considered to be free from potential personal interpretation. According to hi definition, “fingerprints, by their nature, form special drawings from birth; these will not change throughout life and will be absolutely different from one human being to another”. This fingerprint identification method was immediately refined by Giovanni Gasti, whom Salvatore Ottolenghi had chosen as his personal assistant at the Scuola di Polizia Scientifica (School of Forensic Science). Gasti, adapting the classification method of Francis Galton and Edward Henry, developed the “Sistema Gasti (Gasti System)”, which was in use throughout the 1900s.

Multi-information fingerprint identification method based on interactive genetic algorithm

Multi-information fingerprint identification method based on interactive genetic algorithm

Multi‐Stimuli Responsive and Multicolor Adjustable Pure Organic Room Temperature Fluorescence‐Phosphorescent Dual‐Emission Materials

AbstractA simple bromine and cyanogen substituents positional isomeric 9‐phenyl‐9H‐carbazole (PhCz) donor–acceptor system (namely p‐NN‐Br and m‐NN‐Br) displays ultralong organic phosphorescence, and exhibits unique dual emission properties (fluorescence and phosphorescence). These molecules are found to display distinct responses toward multiple external stimuli including temperature (T), excitation light intensity (I) and pressure (P), and show multicolor tunable behaviors (including the white light, the Commission Internationale d'Eclairage (CIE) = 0.33, 0.34). The unique stimuli‐triggered proportion between singlet and triplet excitons for p‐NN‐Br and m‐NN‐Br is demonstrated systematically by investigating the photophysical spectrum, scanning electron microscope (SEM) imaging and X‐ray analysis, coupled with theoretical calculations. They reveal that the simultaneous introduction of halogens (Br) and pseudohalogens (CN) to the PhCz skeleton can improve the intermolecular interaction and thermal stability. Single crystal analysis shows that there are many more types of dimers and J‐aggregates, thereby stabilizing the excitation of triplet states. Moreover, these isomers have “latent” fingerprint recognition and anti‐background interference performance, which is expected to provide a new method for fingerprint identification. All in all, this strategy paves the way to a multifunctional platform for the development of multi‐stimuli responsive, multicolor regulation and smart luminescent materials with long‐lived emission at room temperature.

On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method.

In this paper, a novel device identification method is proposed to improve the security of Visible Light Communication (VLC) in 5G networks. This method extracts the fingerprints of Light-Emitting Diodes (LEDs) to identify the devices accessing the 5G network. The extraction and identification mechanisms have been investigated from the theoretical perspective as well as verified experimentally. Moreover, a demonstration in a practical indoor VLC-based 5G network has been carried out to evaluate the feasibility and accuracy of this approach. The fingerprints of four identical white LEDs were extracted successfully from the received 5G NR (New Radio) signals. To perform identification, four types of machine-learning-based classifiers were employed and the resulting accuracy was up to 97.1%.

Radio Frequency Fingerprint Identification Based on Deep Complex Residual Network

Radio frequency fingerprint identification is a non-password authentication method based on the physical layer hardware of the communication device. Deep learning methods provide new ideas and techniques for radio frequency fingerprint identification. As a bridge between electromagnetic signal recognition and deep learning, the electromagnetic signal recognition method based on statistical constellation still needs to go through data preprocessing and feature engineering, which is contrary to the end-to-end learning method emphasized by deep learning. Moreover, in the process of converting electromagnetic signal waveform data into images, there is inevitably information loss. Establishing a universal radio frequency fingerprint recognition model suitable for wireless communication scenarios is not only conducive to optimizing the communication system, but also can reduce the cost and time of model selection. Therefore, how to design a deep learning radio frequency fingerprint recognition model suitable for wireless communication is an important problem for researchers. Aiming at the problem that the existing radio frequency fingerprint extraction and identification methods have low recognition rate of communication radiation source individuals, a radio frequency fingerprint identification method based on deep complex residual network is proposed. Through the deep complex residual network, the radio frequency fingerprint feature extraction of the communication radiation source individual is integrated with the recognition process, and an end-to-end deep learning model suitable for wireless communication is established, which greatly improves the identification accuracy of the communication radiation source individuals compared with typical constellation based methods.