Fingerprint Identification Research Articles

Data-Enhancement-Aided Protocol-Agnostic Transmitter Recognition for Open-Set in IoT

Radio Frequency Fingerprinting Identification (RFFI), based on hardware inherent imperfections, is an effective technique for physical authentication in the Internet of Things (IoT). However, due to the nonstationary and open factors in realistic scenes, problems such as inaccurate fingerprinting extractions and unreliable transmitter identifications often occur. Specifically, in actual scenarios, there are challenges such as varying channel status, variable transmission content, insufficient dataset acquisition, and unknown number of rogue devices. In this article, a robust and protocol-agnostic RFFI system is proposed for open-set recognition, which is achieved by a slicing-enhanced preprocessing with noise augmentation and a well-designed bone network with a modified open layer. Moreover, in order to verify the effectiveness and transferability of the designed RFFI system in applications of IoT, comparative experiments are conducted using a real-world dataset of Automatic Dependent Surveillance-Broadcast (ADS-B) signals and the open-source FIT/CorteXlab dataset. The performance of the designed extractor and classifier, under varying channel and openness conditions, verifies the channel feasibility and openness generalization of our designed system. The results of the ablation experiment indicate that the designed RFFI system can achieve an accuracy of 96.28% in the classification of 50 registered IoT transmitters and 87.52% in open-set recognition of mixed 50 registered and 10 unregistered IoT transmitters.

Highly Sensitive and Selective Detection of Pharmaceuticals on Au/MIL-101(Cr) by SERS.

The detection of pharmaceuticals has been a matter of concern among scientists and health researchers in the past few decades. However, it is still difficult to realize the sensitivity and selectivity detection of pharmaceuticals with similar structures. Herein, the pharmaceutical molecules of 2-mercaptobenzimidazole (MBI) and 2-mercaptobenzothiazole (MBT) with so similar structures can be selectively detected by surface-enhanced Raman spectroscopy (SERS) taking advantage of the fingerprint identification on Au/MIL-101(Cr), with sensitive detection limits of 0.5 ng·mL-1 for MBI and 1 ng·mL-1 for MBT. MBI is selectively enriched by Au/MIL-101(Cr) from the mixture solution and detected by SERS below 30 ng·mL-1. MBI can also be selectively detected in the serum samples with a detection limit of 10 ng·mL-1. Density functional theory calculations combined with the SERS experiments explained that the high sensitivity and selectivity are caused by the intrinsic differences in Raman intensity and different adsorption energies from the pharmaceutical molecules adsorbed on Au/MIL-101(Cr), respectively. This study provides an effective way to enrich and detect pharmaceutical molecules with similar structures.

Post-Synthetic Modification of a Fluorescent Covalent Organic Framework as a Multifunctional Sensor for Efficient Detection of 5-Hydroxytryptamine and Its Metabolite and Latent Fingerprint Identification

In this study, a functionalized vinyl-linked 2D fluorescent covalent organic framework (COF) (TMT-TA-COOH) is constructed by post-synthetic modification (PSM) of a TMT-TA COF, which is composed of trimethyltriazine (TMT) and terephthalaldehyde (TA). The fluorescent COF (TMT-TA) has the characteristics of good crystallization and stability, but the dispersibility in water is poor, which requires further PSM. After PSM, the obtained TMT-TA-COOH not only exhibits good dispersibility in water but also displays stronger binding ability to analytes, which presents high sensitivity for quantitative identification of 5-hydroxytryptamine (5-HT) and its metabolite 5-hydroxyindole-3-acetic acid (5-HIAA) in water. Moreover, TMT-TA-COOH can specifically recognize 5-HT in serum and 5-HIAA in urine with a fast response, good anti-interference performance, and real-time visualization, which enables the TMT-TA-COOH to be a tool for early detection of neurological diseases and biomarkers for carcinoid tumors. In addition, TMT-TA-COOH is also a good candidate for latent fingerprint (LFP) identification, which has the characteristics of high contrast and low background interference. This work not only provides a promising platform for the sensing application of fluorescent COFs in aqueous solution but also opens up a new way for the detection of LFP information.

An Improved Quality Enhancement Fingerprint Analysis

The use of fingerprint authentication as a biometric technique for secure authentication and access control has gained significant traction in recent years. This article presents an overview of the technology used for fingerprint identification, its history, as well as the fundamental components that make up a fingerprint recognition system. We also go through the many approaches to fingerprint identification techniques and algorithms, such as the minutiae-based approach, the ridge feature-based approach, and the pattern recognition-based approach. All of these are utilized in the process of authenticating fingerprints. In addition, we emphasize the limitations of fingerprint authentication as well as the security risks that are linked with it, as well as alternative methods to overcome these constraints.

Establishment of X-ray diffraction fingerprints for identification of different configuration Realgar and its antitumor activity

Establishment of X-ray diffraction fingerprints for identification of different configuration Realgar and its antitumor activity

A-Survey: Identification and Classification of Fingerprints via the Extreme Learning Machine Algorithm

The fingerprint comes to be the most popular and utilized biometric for identifying persons owing to its bio-invariant characteristic, precision, as well as easy acquisition. A sub-system of an identification system is the classification stage in order to diminish the penetration rate and computational complexity. Actually, there are many formal investigations regarding techniques by exploiting convolutional neural networks (CNN) together with fingerprint images, which have superior performance metrics at the cost of large training times even employing high-performance computing, which is not feasible in the standard world. In our manuscript, researches about identifying and classifying fingerprint databases by recurring to extreme learning machines (ELM) will be extensively reported and discussed for the first time. The diverse methodologies (ELM plus feature extractors) given by the authors will be studied and contrasted considering performance analysis. Consequently, academic papers with diverse versions of ELMs are developed to observe the pros and cons that they exhibit with each other and to probe how they may help for minimizing the penetration rate of fingerprint databases. In fact, this issue is very relevant because enhancing the penetration rate means shorting search times and computational complexity in fingerprints.

The earliest evidence of domestic chickens in the Japanese Archipelago

The chicken (Gallus gallus domesticus) is the most conventional domestic animal whose main ancestor is the red junglefowl, found in Southeastern Asia and the southern part of China. Chickens were believed to have been brought to the Japanese Archipelago through the Korean Peninsula during the Yayoi period, but its exact age is unknown. Based on the sexual dimorphism of morphology, we pointed out that most chickens in the Yayoi period were males and that they were rarely bred in Japanese Archipelago. During the 58th survey of the Karako-Kagi site (Tawaramoto Town, Nara Prefecture), four pieces of immature Phasianidae bone were excavated from a division groove dating from the early middle Yayoi period. In this study, we performed collagen peptide fingerprinting identification and radiocarbon dating of immature Phasianidae bones from the Karako-Kagi site. Consequently, two peptide mass peaks unique to chickens were observed in samples from the immature bones, which were revealed to be derived from immature chickens. The calibrated age of the sample was confirmed to be between the fourth and third century BCE, which coincided with the opening age of the division groove. These results suggest that chickens have been successively bred since the beginning of the middle Yayoi period, at least in the Karako-Kagi village. The date was regarded as the lower limit for the introduction of chickens into the Japanese Archipelago, Korean Peninsula, and East Asia.

WiCrew: Gait-Based Crew Identification for Cruise Ships Using Commodity WiFi

Security check-in life-support areas, e.g., bridge and engine room are crucial for cruise ships due to numerous and diverse passenger identities. Instead of conventional security check approaches, such as facial recognition and fingerprint identification, device-free approaches enabled by WiFi-based gait recognition have attracted considerable attention owing to their low cost, nonintrusiveness, and privacy protection. Despite the excellent performance of existing indoor methods, they cannot be trivially extended to cruise ships because of the unique characteristics of hull deformation caused by vibrating engines and waves. This stems from the flexible structure of cruise ships, which introduces additional noise to the WiFi signals. To address this challenge, we propose WiCrew, a device-free gait recognition system that detects crew identity anomalies in cruise ships. WiCrew consists of two components: 1) a spatial separation algorithm that separates the signal components from ship vibration and human activity and 2) a speed-independent adversarial learning framework that identifies the ship’s crew using human gaits at an arbitrary walking speed. Extensive experiments on a cruise ship demonstrate the effectiveness of WiCrew. While the crew members walk at speed of 0.7 to 1.8 m/s, the average recognition accuracy reaches 82%, which is similar to vision-based approaches.

Design of a Channel Robust Radio Frequency Fingerprint Identification Scheme

Radio frequency fingerprint (RFF) identification is an emerging device authentication technique that exploits the hardware imperfections resulting from the manufacturing process. Due to the varying impact of the wireless channel during RFF training and test stages, it is challenging to design channel-independent RFF techniques. This article designs a channel robust RFF identification scheme by leveraging the different spectrum of adjacent signal symbols, named the Difference of the Logarithm of the Spectrum (DoLoS), which does not rely on a single RFF feature or requires additional manipulation of the devices under test. Specifically, DoLoS exploits the fact that two different symbols in a packet exhibit different RFF features but have a similar channel response during the channel coherence time. We implemented the DoLoS with the IEEE 802.11 orthogonal frequency division multiplexing (OFDM) system as a case study. We carried out extensive experiments using seven Wi-Fi devices of the same model in different wireless channel environments, including 12 data collection positions in two completely different environments. Compared with conventional RFF identification schemes that do not eliminate channel effects, our scheme is robust to channel variations and the highest identification accuracy is 99.02% in the single-environment evaluation and 97.05% in the cross-environment evaluation.

Rethinking Social Security Protection with a Digital Mindset: Cases of PSSSF and NSSF in Tanzania

This study examined the use of computerized systems in Social Security Funds (SSFs) in Tanzania. The primary data used for the study were derived using structured questionnaires. Descriptive statistics was used in data analysis. Using a case study design within the quantitative approach, this study examined the types of computerized systems used in Social Security Funds. Findings revealed that the Social Security Scheme in Tanzania is undergoing transformation. It is moving towards a ‘<i>Digital Pension</i>.” In many SSF offices computers, networks, the Internet, and World Wide Web are increasingly used to capture, process, store, and track members’ remittances. More specifically, computerized systems used include Members Management Information System (MMIS), Core Fund Management System (CFMS), “<i>Mobile Taarifa”</i> through mobile phones, Automated Finger Print Identification Systems, Information Kiosks, self-help portal, benefits calculators and Fund Identification Management System (FIMS). They relieve members from visiting and queuing for long hours at SSF offices for a service they would get in a minute all by themselves. Computerization is mostly used for improving the process of issuing benefits. There is a need for using technology for increasing coverage. The government should encourage citizens to use mobile phones to open their pension accounts linked to National IDs.

Radio Frequency Fingerprint Identification Based on Metric Learning

With the popularization of the internet of things (IoT), its security has become increasingly prominent. Radio-frequency fingerprinting (RFF) is used as a physical-layer security method to provide security in wireless networks. However, the problems of poor performance in a highly noisy environment and less consideration of calculation resources are urgent to be resolved in a practical RFF application domain. The authors propose a new RFF identification method based on metric learning. They used power spectrum density (PSD) to extract the RFF from the nonlinearity of the RF front end. Then they adopted the large margin nearest neighbor (LMNN) classification algorithm to identify eight software-defined radio (SDR) devices. Different from existing RFF identification algorithms, the proposed LMNN method is more general and can learn the optimal metric from the wireless communication environment. Furthermore, they propose a new training and test strategy based on mixed SNR, which significantly improves the performance of conventional low-complexity RFF identification methods. Experimental results show that the proposed method can achieve 99.8% identification accuracy with 30dB SNR and 96.83% with 10dB SNR. In conclusion, the study demonstrates the effectiveness of the proposed method in recognition efficiency and computational complexity.

Latent Fingerprint Development by Brick Powder

In forensic investigations, the relationship between the criminal, the victim, and the crime scene can be firmly established through the detection of latent finger marks. Fingerprints are avital and unique identity of a person. These are impressions left by the friction ridges of a human finger on contact with any surface. Many unique characteristics in the human body like a fingerprint, DNA, palm prints, iris patterns, voices, etc. So, Fingerprint is a unique characteristic of the human body, and every human’s different fingerprints between identical twins. As we know friction ridges develop in a 3-4 month of gestation duration time. Mayer, a German doctor, and anatomist were the first to write that friction ridge skin is unique. The fingerprint is the most important evidence at the Crime scene and identification of the criminal. Fingerprint identification is a method of identification using the impression formed by the minute ridges seen on the fingertips. Fingerprint has certain individual characteristics that are unique. Latent fingerprints are most generally seen prints from the crime scene. Such prints are obtained when natural oils and perspiration existing between the fingertip ridges are transferred to a surface by any contact. Latent prints are invisible and are much harder to detect and preserve. In this research paper, a new method for the development of Latent fingerprints is used that’s brick powder. The brick powder is used in various Porous and Non-porous surfaces like plastic glass, ordinary mirror, matchbox, painted wall, writable CD, wooden substrate, currency note, door handle, floor surface, book cover, sunglasses, and mobile glass card for development of latent fingerprint impression.

Smart Voting System

The electoral system in India has undergone several changes in recent years. The current system involves either Secret Ballot Voting (SBV) or Electronic Voting Machines (EVM), both of which are costly, labor-intensive and inefficient. The current system only verifies identification proof, which increases the likelihood of fraudulent voting. This study proposes the use of an electronic voting system that incorporates fingerprint and face recognition technology. The online system enables voters to cast their votes from anywhere, minimizing the chances of voting fraud. The proposed system has three levels of security: verification of unique id number, election id number, and face recognition. The use of voter identity number and fingerprint can prevent fake and duplicate registrations, leading to a higher success rate during voting. This method significantly enhances the security level of the voting process. Key Words: identification proof, face recognition, protect, security level, success.

Abstract 2151: Identification of transcript adenosine fingerprint to enrich for A2AR and PD-1 inhibition responders

Abstract Background: Precision cancer medicine aims to identify the right drug for the right patient, enriching for patients more likely to respond to a particular treatment. This paradigm is gaining importance during the early clinical lifecycle of a new potential drug to improve patient-centric trial designs, drive clinical success and eventually increase approval rates - enlarging the therapeutic arsenal available for oncology patients. To optimize the chance of success with our A2AR-selective antagonist, EXS-21546 (546; NCT04727138, discovered in collaboration with Evotec), we have identified an adenosine-induced immunosuppression biomarker signature (adenosine burden score or ABS) for clinical trial patient selection that also correlates with checkpoint inhibitor (CI) response prediction in ex vivo primary models. Here we present transcriptional and functional data mapping adenosine burden at the single cell level, and investigate subsequent modulation through antagonism of A2AR with 546, combination effects with CI, to prioritize patients for 546+CI therapy. Methods: By leveraging disease-relevant primary human tissues together with matched single cell and bulk transcriptomics, we assess adenosine-induced anticancer immune suppression and show initial biological confirmation of patient selection methodology and combination therapy effects with a translatable high content imaging platform (Kornauth et al 2021 &amp; Snidjer et al 2017). Results: The ABS detects adenosine rich microenvironments with greater specificity and sensitivity than other published signatures. Validating the ABS in TGCA, we found the ABS anti-correlates with a validated predictor of anti-PD-1 therapy success (TIS, Damotte et al 2019), unraveling that high-adenosine/ABS cases are among patients least likely to respond to immunotherapy (low TIS). A2AR antagonism with 546 demonstrated a reduction of the adenosine burden, and restored the CI response potential as addressed by the ABS and TIS, respectively. Further immune reactivation was seen with antagonism of adenosine signaling by ‘546/CI combination ex vivo in primary tissues pre-selected with our ABS signature. Conclusions: Combining deep learning of single cell functional and multi-omics profiling data of disease relevant primary model systems, we model the association of the immune response potential to A2AR antagonism in cancer to define a biomarker signature to predict patients likely to benefit from A2AR antagonism and CI. This will be confirmed and validated retrospectively in an ongoing clinical study of 546 in two cancer indications. Citation Format: Isabella Alt, Robert Sehlke, Anna Lobley, Claudia Baumgaertler, Maja Stulic, Klaus Hackner, Lucia Dzurillova, Edgar Petru, Laudia Hadjari, Judith Lafleur, Josef Singer, Nikolaus Krall, Jozef Šufliarsky, Lukas Hefler, Thorsten Füreder, Christina Taubert, Andrew Payne, Christophe Boudesco, Gregory Ian Vladimer. Identification of transcript adenosine fingerprint to enrich for A2AR and PD-1 inhibition responders [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2151.

A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion

The multimedia content generated by devices and image processing techniques requires high computation costs to retrieve images similar to the user’s query from the database. An annotation-based traditional system of image retrieval is not coherent because pixel-wise matching of images brings significant variations in terms of pattern, storage, and angle. The Content-Based Image Retrieval (CBIR) method is more commonly used in these cases. CBIR efficiently quantifies the likeness between the database images and the query image. CBIR collects images identical to the query image from a huge database and extracts more useful features from the image provided as a query image. Then, it relates and matches these features with the database images’ features and retakes them with similar features. In this study, we introduce a novel hybrid deep learning and machine learning-based CBIR system that uses a transfer learning technique and is implemented using two pre-trained deep learning models, ResNet50 and VGG16, and one machine learning model, KNN. We use the transfer learning technique to obtain the features from the images by using these two deep learning (DL) models. The image similarity is calculated using the machine learning (ML) model KNN and Euclidean distance. We build a web interface to show the result of similar images, and the Precision is used as the performance measure of the model that achieved 100%. Our proposed system outperforms other CBIR systems and can be used in many applications that need CBIR, such as digital libraries, historical research, fingerprint identification, and crime prevention.

Utilizing Convolutional Neural Networks for Fingerprint-Based Attendance Monitoring

The traditional method of taking attendance using paper sheets is prone to errors like impersonation, loss, or theft. To solve this issue, automatic attendance systems utilizing identification technology such as barcode badges, electronic tags, touch screens, magnetic stripe cards, and biometrics have been implemented. Biometric technology uses physiological or behavioral characteristics for identification purposes, but traditional biometric systems have limitations such as vulnerability to damage or alteration over time, and variations in occlusions, poses, facial expressions, and illumination can affect face recognition accuracy. Fingerprint identification relies on the distinctiveness of fingerprints and involves comparing two impressions of the friction ridges on human fingers or toes to determine if they belong to the same individual. There are five primary categories of fingerprints: arch, tented arch, left loop, right loop, and whorl. Various algorithms have been developed to recognize fingerprints using minutiaebased matching, which involves identifying key features like ridge ending and bifurcation. Deep learning algorithms, particularly convolutional neural networks, have been successful in improving identification accuracy by extracting features automatically from fingerprint images. In recent times, securing personal data has become increasingly important, and the Convolutional Neural Network (CNN) identification system is recommended for improving accuracy and performance. This paper proposes a fingerprint identification system that combines three models: CNN, Softmax, and Random Forest (RF) classifiers. The conventional system uses K-means and DBSCAN algorithms to separate the foreground and background regions and extracts features using CNNs and dropout approach. The Softmax acts as a recognizer. The proposed algorithm is evaluated on a public database and shows promising results, providing an accurate and efficient biometric identification system.

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