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Background: Recent advances have increased the importance of the human microbiome, including the skin microbiome. Despite the hand microbiome research, the factors affecting the composition of the hand microbiome and their personal characteristics are incompletely known. Objectives: Despite changing environmental factors and personal variation, we aimed to indicate the interpersonal distinction between skin microbiota using simple and rapid molecular methods. Methods: Over a non‐consecutive 10‐day period, samples were taken from 10 adult individuals, and ribotyping analysis of the 16S and 23S genes of S. epidermidis was performed on each skin sample. Additionally, EcoRI and HindIII enzyme reactions and variable number tandem repeat (VNTR) reactions of S. epidermidis obtained from DNA samples were performed. The skin microbiomes of individuals were evaluated along with the microbiome profiles left on the surfaces they touched. Results: In the environmental samples taken, it has been observed that people preserve their core skin microbiota characters and carry them to their environment. It was determined that the highest similarity rate was 77.14%, and the lowest similarity rate was 31.74%. Conclusion: Our study showed that the core skin microbiota retains its characteristics and leaves traces in environments. The fact that the personal microbiome remains unchanged despite environmental differences and has characteristic features has shown that it can be used in forensic sciences to distinguish individuals from each other. These results with simple and rapid methods further increased the importance and significance of the study. The findings indicate that personal skin microbiota can provide a significant contribution to criminal investigations by increasing accuracy and reliability, especially in forensic analyses. [ABSTRACT FROM AUTHOR]

Postmortem fingerprinting is the fastest and most reliable way to scientifically identify unknown decedents from mass fatality incidents. In disaster victim identification (DVI), fingerprints, DNA, and dental examinations are the three primary methods of identification. Additional secondary identifiers such as comparative medical radiography, scars, marks, and tattoos can also be used if viable antemortem information can be located. As DNA technology continues to evolve, RAPID DNA may now identify a profile within 90 min if the remains are not degraded or comingled. When there are true unknowns, however, there is usually no DNA, dental, or medical records to retrieve for a comparison without a tentative identity. It is imperative to understand how to properly collect postmortem prints following various postmortem changes, which databases are available to search against, and what additional resources are available prior to any such event occurring. With advances in technology, automated fingerprint searching is faster than ever before. Mobile devices can now search all the major databases within a matter of minutes from a cell phone or tablet utilizing a peripheral fingerprint scanner. Preliminary research into the application of WD‐40® when utilizing optical‐capacitive scanners has shown to greatly increase the efficacy of capturing usable fingerprints. This additional step allows for the digital capture of fingerprints in even complicated settings, both in daily cases and DVI incidents. This article outlines all the available ways to obtain postmortem fingerprints in complex cases, and how they can be applied to efficiently and effectively used in the DVI process. [ABSTRACT FROM AUTHOR]

Fingerprint recognition systems hold a pivotal role across various modern applications, such as criminal investigation, civil identification, and access control. Fingerprint segmentation is commonly the first stage of fingerprint recognition systems, focusing on extracting the foreground from captured fingerprint images. This helps to reduce extracting false minutiae, speed up the extraction process, and hence, improve the overall system performance. In this paper, we introduce a novel fingerprint segmentation method by first proposing new frequency and intensity features with employing fuzzy-c-means and genetic algorithm, accompanied by proposing a new cluster assignment strategy that involves features weighting and cluster assignment probabilities thresholding which helps to improve the accuracy compared to the standard cluster assignment method. Finally, we introduce a new morphological post-processing technique in order to minimize the misclassified pixels and obtain the final mask. Furthermore, we carry out a comprehensive performance evaluation against leading fingerprint segmentation methods on various known databases. As a result, the proposed method achieves an average error rate of 2.52%, which is much lower than the corresponding errors obtained in the literature. By these experimental results, we show that the proposed method outperforms the best existing methods. [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Biometric devices have been indicated to be vulnerable to various threats. In the present study, a novel liveness identification technique is introduced according to the noise analysis of fingerprint images. It has been shown that the textural patterns of real fingertips are different from those of spoof fingertips. In this work, Real images were successfully distinguished from fake ones with an accuracy of approximately 99.50%. The database consists of 200 fingerprints, half of which is the real database (i.e. 100 fingerprints), and the other half is the fake database. Textural features are analyzed using pyramidal mean and PDFB analysis techniques that decompose images into directional sub-bands at several scales. The Shannon entropy, approximate entropy, sample entropy, permutation entropy, fuzzy entropy, multiscale entropy, Log energy entropy, and Sure entropy were estimated for all these sub-bands and were arranged into an array. In the classification stage, an ensemble classifier based on a multiobjective genetic algorithm was employed. The results obtained revealed that the suggested technique could successfully differentiate live fingertips from fake ones. The best accuracy was obtained for the proposed ensemble technique, with an average accuracy of 99.52% compared to other classifiers. After the ensemble method, quadratic SVM achieved the best classification performance with a mean accuracy of 97.34% in discriminating real live fingerprints from fake fingerprints. In comparison to other prior techniques, the suggested method produced good classification performance in real-time fingerprint identification while remaining straightforward. Therefore, the system can be a good option for use in real biometric systems. [ABSTRACT FROM AUTHOR]

Fingerprint and iris traits are used in sensitive applications and so, spoofing them can impose a serious security threat as well as financial damages. Spoofing is a process of breaking biometric security using artificial biometric traits. This spoofing can be avoided by detecting the liveness of the biometric traits. Hence, liveness detection techniques have become an active research area. However, liveness detection techniques are also prone to attack because of advanced spoofing materials. Hence, they are subjected to further development to face futuristic spoofing and compromising real biometric traits. To aid the development, this paper technically and informatically reviews the state-of-the-art liveness detection techniques in the last decade. Firstly, the paper reviews the processing strata, adopted features and detectors in the existing liveness detection techniques. Secondly, the paper presents the benchmark datasets, their characteristics, availability and accessibility, along with the potential spoofing materials that have been reported in the literature under study. Thirdly, the survey reports the performance of the techniques on the benchmark datasets. Eventually, this paper summarizes the findings, gaps and limitations to facilitate strengthening of liveness detection techniques. This paper further reports that the Fingerprint Liveness Detection (FLD) techniques such as Slim-ResCNN, JLW and Jung CNN have achieved a better accuracy of 94.30%, 98.61% and 97.99%, respectively on LivDet19 datasets. It has been observed that CNN-based architectures have outperformed in significant number of FLD datasets. In contrast, Support Vector Machine (SVM) with appropriate shallow and deep features has achieved equivalent performance against deep classifiers on detecting iris spoofs from Iris Liveness Detection (ILD) datasets. [ABSTRACT FROM AUTHOR]

The biometrics system is mainly focuses on fingerprint recognition and how this system would be implemented. If it is implemented in research area, this system would authenticate or contradict the uniqueness of each individual endeavouring to gain access to the research region with a given sum of precision. The method of authenticating a person's identity through a comparison of their fingerprints with previously captured samples is known as fingerprint recognition. Human finger impressions in the form of ridges and grooves are called fingerprints. The minutiae points, or the places where scars start or end, are used to locate fingerprints stored in the system. To create a minutiae template, these are further mapped with lines connecting them. An additional key challenge of this will understand the methods used to analysed the information from the scan. There are several different methods has been implemented by different researchers for the fingerprint recognition system. In this paper the literature of the different approaches are discussed. [ABSTRACT FROM AUTHOR]

Estimation of gender, hand, and finger to minimize the probable suspects list in a fingerprint database search is a very important stride in forensic anthropology. Previous research attempted to estimate the gender, hand, and finger from the fingerprint, but the results were not consistent. In this effort, we proposed gender, hand, and finger estimation based on fingerprints using a deep convolution neural network. The publicly available SOCOFIG dataset which embraces 55222 no of fingerprints, is used for training and evaluation of the proposed procedure. On the aforementioned dataset, the suggested mode of operation achieves 99.38% gender, 99.46% hand, and 97.36% finger prediction validation accuracy. The results are competitive and commendable when compared to the preceding techniques. [ABSTRACT FROM AUTHOR]

Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment. [ABSTRACT FROM AUTHOR]

Fingerprints hold evidential value for individual identification; a sensitive, efficient, and convenient method for visualizing latent fingermarks (LFMs) is of great importance in the field of crime scene investigation. In this study, we proposed an aggregation-induced emission atomization technique (AIE-AT) to obtain high-quality fingermark images. Six volunteers made over 1566 fingerprint samples on 17 different objects. The quality of fingermark development was evaluated using grayscale analysis for quantitative assessment, combining the fluency of fingermark ridges and the degree of level 2 and level 3 features. Both qualitative and quantitative methods were employed to explore the effectiveness of AIE molecule C27H19N3SO in developing fingermarks, its applicability to objects, and its individual selectivity. Additionally, the stability of the AIE molecule was examined. Comparative experimental results demonstrated the high stability of the AIE molecule, making it suitable for long-term preservation. The grayscale ratio of the ridges and furrows was at least 2, with high brightness contrast, the level 2 and level 3 features were clearly observable. The AIE-AT proved to be effective for developing fingermarks on nonporous, porous, and semiporous objects. It exhibited low selectivity on suspects who leave fingermarks and showed better development effects on challenging objects, as well as efficient extraction capability for in situ fingermarks. In summary, AIE-AT can efficiently develop latent fingermarks on common objects and even challenging ones. It locates the latent fingermarks for further accurate extraction of touch exfoliated cells in situ, providing technical support for the visualization of fingermarks and the localization for extraction of touch DNA. [ABSTRACT FROM AUTHOR]

The intelligent reflective surface (IRS) is a novel network node that consists of a large-scale passive reflective array to obtain a customized reflected wave direction by modulating the amplitude phase, which can be easily deployed to change the wireless signal propagation environment and enhance the communication performance under a non-line-of-sight (NLOS) environment, where location services cannot perform accurately. In this study, a low-rank matrix reconstruction-enabled fingerprint-based localization algorithm for IRS-assisted networks is proposed. Firstly, a 5G positioning system based on IRSs is constructed using multiple IRSs deployed to reflect signals. This enables the base station to overcome the influence of NLOS and receive the positioning signal of the point to be positioned. Then, the angular domain power expectation matrix of the received signal is extracted as a fingerprint to form a partial fingerprint database. Next, the complete fingerprint database is reconstructed using the low-rank matrix fitting algorithm, thereby considerably reducing the workload of building the fingerprint database. Finally, maximal ratio combining is used to increase the gap between the fingerprint data, and the Weighted K-Nearest Neighbor (WKNN) algorithm is used to match the fingerprint data and estimate the location of the points to be located. The simulation results demonstrate the feasibility of the proposed method to achieve sub-meter accuracy in an NLOS environment. [ABSTRACT FROM AUTHOR]

Fingerprint recognition systems have achieved widespread integration into various technological devices, including cell phones, computers, door locks, and time attendance machines. Nevertheless, individuals with worn fingerprints encounter challenges when attempting to unlock original fingerprint systems, which results in disruptions to their daily activities. This study explores two distinct methods for fingerprint backup: traditional fingerprint impression and 3D printing technologies. Unlocking tests were conducted on commonly available optical fingerprint lock-equipped cell phones to assess the efficacy of these methods, particularly in unlocking with worn fingerprints. The research findings indicated that the traditional fingerprint impression method exhibited high fidelity in reproducing fingerprint patterns, achieving an impressive unlocking success rate of 97.8% for imprinting unworn fingerprints. However, when dealing with worn fingerprints, the traditional fingerprint impression technique showed a reduced unlocking success rate, progressively decreasing with increasing degrees of finger wear. In contrast, 3D-printed backup fingerprints, with image processing and optimization of ridge height, mitigated the impact of fingerprint wear on the unlocking capability, resulting in an unlocking success rate of 84.4% or higher. Thus, the utilization of 3D printing technology proves advantageous for individuals with severely worn or incomplete fingerprints, providing a viable solution for unforeseen circumstances. [ABSTRACT FROM AUTHOR]

Principal Component Analysis (PCA) is the best face recognition method. This research suggests PCA for fingerprint and signature recognition. Simple image processing transforms like DCT, 2D-DCT, DWT, SWT, 2D-SWT, SVD (Singular Vector Decomposition), Entropy, and Rank can be used for feature extraction. These transforms and measures are utilized with PCA as a feature extraction module to construct uni-modal and multimodal biometric systems using face, fingerprint, and signature modalities. Most PCA biometrics systems compare stored template to claimed identification using Euclidean distance. This paper proposes matching modules using similarity and dissimilarity measures viz. Absolute Pearson's Correlation Coefficient (APCC), Absolute Uncentered Pearson's Correlation Coefficient (AUPCC), Bray Curtis Distance (BC), Canberra distance (CB), Chebyshev Distance (CBS), Chessboard Distance (CSB), City block or Manhattan distance (CTB), Cross Correlation (CC), Dot product (DP), Euclidean distance (EUC), Extended Jaccard Distance (EJ), Hamming Distance (HM), Harmonically Summed Euclidean distance (HSEUC), Kendall Correlation Coefficient (KCC), Mahalanobis Distance (MH), Minimum Coordinate Difference (MCD), Minkowiski distance (MNK), Multivariate Kurtosis Coefficient (MVK), Multivariate Skew (MVS), Normalized City Block or Manhattan distance (NCTB), Normalized Cross-correlation (NCC), Normalized Euclidean distance (NEUC), Pearson's Cosine Distance (PCOS), Pearson's Correlation Coefficient (PCC), Pearson's Absolute Value Dissimilarity (PAVD), Pearson's Linear Dissimilarity (PLDISS), Spearman Correlation Coefficient (SCC), Standardized Euclidean Distance (SEUC), Uncentered Pearson's Correlation Coefficient (UPCC), Wave-Hedges Distance (WVH). This study again discusses score level fusion of face, fingerprint, and signature using sum and max rules, z-score normalization, and decision level fusion using AND rule. [ABSTRACT FROM AUTHOR]

Natural products are a diverse class of compounds with promising biological properties, such as high potency and excellent selectivity. However, they have different structural motifs than typical drug-like compounds, e.g., a wider range of molecular weight, multiple stereocenters and higher fraction of sp3-hybridized carbons. This makes the encoding of natural products via molecular fingerprints difficult, thus restricting their use in cheminformatics studies. To tackle this issue, we explored over 30 years of research to systematically evaluate which molecular fingerprint provides the best performance on the natural product chemical space. We considered 20 molecular fingerprints from four different sources, which we then benchmarked on over 100,000 unique natural products from the COCONUT (COlleCtion of Open Natural prodUcTs) and CMNPD (Comprehensive Marine Natural Products Database) databases. Our analysis focused on the correlation between different fingerprints and their classification performance on 12 bioactivity prediction datasets. Our results show that different encodings can provide fundamentally different views of the natural product chemical space, leading to substantial differences in pairwise similarity and performance. While Extended Connectivity Fingerprints are the de-facto option to encoding drug-like compounds, other fingerprints resulted to match or outperform them for bioactivity prediction of natural products. These results highlight the need to evaluate multiple fingerprinting algorithms for optimal performance and suggest new areas of research. Finally, we provide an open-source Python package for computing all molecular fingerprints considered in the study, as well as data and scripts necessary to reproduce the results, at https://github.com/dahvida/NP_Fingerprints. [ABSTRACT FROM AUTHOR]

Biometric technology, founded on recognizing distinctive individual features, enhances identification and automation, yielding personal and institutional benefits. This literature review employs the Kitchenham and Charters methodology to analyze recent perspectives on biometric technology in educational time and attendance. The analysis identifies benefits, such as preventing impersonation and enhancing data security, but emphasizes concerns about biometric alterations and hardware/software failures hindering optimal outcomes. A noteworthy trend is adopting a bimodal behavioral approach in biometric technologies in education. The study recommends research avenues focusing on privacy safeguards, biometric system robustness, and technological resilience. Beyond mere identification, a shift towards quantitative and descriptive methods is proposed, guiding future research. The results underscore the importance of a multimodal approach and stringent security measures to address biometric system vulnerabilities. Future exploration should consider integrating emerging technologies to enhance system resilience and data protection, providing a comprehensive guide for refining and expanding biometric technology applications in education. [ABSTRACT FROM AUTHOR]

Background: Saikosaponins are regarded as one of the most likely antipyretic constituents of Bupleuri Radix, establishing a comprehensive method that can reflect both the proportion of all constituents and the content of each saikosaponin is critical for its quality evaluation. Methods: In this study, the combination method of quantitative analysis of multiple components with a single marker (QAMS) and fingerprint was firstly established for simultaneous determination of 7 kinds of saikosaponins in Bupleuri Radix by ultra-high performance liquid chromatography (UPLC). Results: The results showed that saikosaponin d was identified as the optimum IR by evaluating the fluctuations and stability of the relative calibration factors (RCFs) under four different conditions. The new QAMS method has been confirmed to accurately quantify the 7 kinds of saikosaponins by comparing the obtained results with those obtained from external standard method and successfully classify the 20 batches of Bupleuri Radix from 8 provinces of China. The experimental time of fingerprint was significantly reduced to approximate 0.5 h through UPLC-PAD method, a total of 17 common peaks were identified. Conclusion: The QAMS-fingerprint method is feasible and reliable for the quality evaluation of Bupleuri Radix. This method could be considered to be spread in the production enterprises of Bupleuri Radix. [ABSTRACT FROM AUTHOR]

Copyright of Basrah Journal of Science / Magallat Al-Barat Li-L-ulum is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of University of Anbar for Pure Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A portable high-field asymmetric ion transfer spectrometer (FAIMS) was miniaturized to improve the design. The prototype consists of a main controller, a galvanometer, a high-voltage square-wave power supply, a Raspberry Pi module, and integrated circuitry. The new design eliminates a main power supply and meets the high- and low-voltage requirements. Qt, a cross-platform software package for creating graphical user interfaces, was used to map the molecular structure fingerprint. The timing, circuit structure, and mechanical assembly were optimized. The dimensions were 45 cm × 48 cm × 23 cm. 16 s were required to collect 1000 data points; the usage time exceeded 8 h, and the background noise was approximately 0.2 pA. The prototype offers excellent efficiency, stability, and multiple applications. The accuracy of FAIMS with the deep-learning model for data acquisition is good. This system is anticipated to evaluate the concentrations of volatiles in diverse types of samples. [ABSTRACT FROM AUTHOR]

Fingerprints are one of the most important types of evidence in crime investigation due to their permanency, uniqueness, and ease of enhancing. Demonstrators usually prefer hiding their identities by covering their faces in illegal events such as throwing stones at security forces or at businesses and residences so as to damage property. Once the fingerprints are detected on stones, it could be possible to reach the perpetrators. However, there is insufficient research about recovering fingerprints on stones and their subsequent reliability. In this study, fingerprints left on 12 different kinds of stone surface were held for 1, 3, and 5 days respectively, after which it was sought to determine whether or not suitable fingerprints could be recovered for comparison. In this study, As a result of various chemical applications in the laboratory on 12 different types of stones, it was revealed that fingerprints with sufficient characteristics could be obtained from various stone types. Fingerprints of good quality were made visible with cyanoacrylate fuming method, particularly on stones with smooth surfaces and minimal porosity such as the outer surfaces of red brick, marble, mosaic, ceramic, tile and granite. After more than a hundred and thirty years, fingerprints still remain to be considered as one of the most important sources of evidence. It is evaluated that fingerprints developed from stones which were thrown during an assault could be instrumental in identification of a perpetrator. [ABSTRACT FROM AUTHOR]

Ultra-large chemical libraries are reaching 10s to 100s of billions of molecules. A challenge for these libraries is to efficiently check if a proposed molecule is present. Here we propose and study Bloom filters for testing if a molecule is present in a set using either string or fingerprint representations. Bloom filters are small enough to hold billions of molecules in just a few GB of memory and check membership in sub milliseconds. We found string representations can have a false positive rate below 1% and require significantly less storage than using fingerprints. Canonical SMILES with Bloom filters with the simple FNV (Fowler-Noll-Voll) hashing function provide fast and accurate membership tests with small memory requirements. We provide a general implementation and specific filters for detecting if a molecule is purchasable, patented, or a natural product according to existing databases at https://github.com/whitead/molbloom. [ABSTRACT FROM AUTHOR]

Oil-bearing Rosa rugosa are popular in the essential oil and perfume markets. The similar botanical characteristics between high-oil-yield or low-oil-yield cultivars are confusing and it is hard for farmers or breeders to identify the high-oil-yield cultivar by phenotype difference. High-resolution melting (HRM) analysis of simple sequence repeats (SSRs) can construct accurate DNA fingerprints quickly, which was shown to be effective for identification of closely related cultivars of R. rugosa. Optimization of HRM-SSR indicated that the 10 µL HRM reaction mixture containing 20 ng of genomic DNA of R. rugosa and 0.75 µL of 10 µmol/L of each primer with an annealing temperature of 64 °C was a robust SSR genotyping protocol. Using this protocol, 9 polymorphic SSR markers with 3–9 genotypes among the 19 R. rugosa cultivars were identified. The top three polymorphic makers SSR9, SSR12 and SSR19 constructed a fingerprint of all cultivars, and the rare insertion in the flanking sequences of the repeat motif of SSR19 generated three characteristic genotypes of three high-oil-yield cultivars. These results may be economical and practical for the identification of high-oil-yield R. rugosa and be helpful for the selection and breeding of oil-bearing roses. [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fingerprints are a vital and unique identity of a person. These are impressions left by the friction ridges of a human finger on contact with any surface. The primary advantage of a fingerprint is that it does not change over time. Fingerprint identification is a method of identification using the impression formed by the minute ridges seen on the fingertips. Fingerprints have certain individual characteristics that are unique. Fingerprints are most generally recovered from crime scenes. Such prints are obtained when natural oils and perspiration (sweat) existing between the fingertip ridges are transferred to the surface by any contact. Fingerprints are invisible and are much harder to detect and preserve. These prints are obtained by physical and chemical methods. In this paper, my research focuses on detecting latent fingerprints or invisible fingerprints using ophthalmic fiber glass powder on different surfaces. Here I used the ophthalmic fiber glass and turned it into powder which is used for obtaining latent fingerprints. [ABSTRACT FROM AUTHOR]

The uniqueness of human fingerprint plays the most prominent role in forensic investigation as a physical evidence Which is useful for criminal identification. With its unique features no two fingerprints have ever been found to be identical. In legal cases the identification of such print established the relationship between crime scene and the culprit. Generally, there are three types of fingerprint is to be found visible print, plastic print and latent print ( invisible print) which has been taken in account for identification. There is no such crucial method is used for decipher the visible print and plastic print because they are easily visible to naked eye but in case of latent print the process for enhancement is necessary for the latent fingerprint to be observed by naked eye. The most encountered fingerprint is the latent fingerprint which is unconsciously left by perpetrators at any surface. For visualizing such prints Various method have been reported for the development of latent fingerprint on different surfaces. This present study introducing a new visualizing agent for the development of latent fingerprint by using Carbon Powder on various non-porous & porous surface through the powdering method. Here we use candle flame to collect black carbon mass and turn it into powder which is used for obtaining latent fingerprints. By the application of these powder it is observed that it gives a clear ridge on various surfaces. [ABSTRACT FROM AUTHOR]

Early bird Template protection using cancelable biometrics prevents data loss and hacking stored templates, by providing considerable privacy and security. Hashing and salting techniques are used to build resilient systems. Salted password method is employed to protect passwords against different types of attacks namely brute-force attack, dictionary attack, rainbow table attacks. Salting claims that random data can be added to input of hash function to ensure unique output. Hashing are speed bumps in an attacker's road to breach user's data. Research proposes a contemporary two factor authenticator called Biohashing. Biohashing procedure is implemented by recapitualted inner product over a pseudo random number generator key, as well as fingerprint features that are a network of minutiae. Cancelable template authentication used in fingerprint-based sales counter accelerates payment process. Fingerhash is code produced after applying biohashing on fingerprint. Fingerhash is a binary string procured by choosing individual bit of sign depending on a preset threshold. Experiment is carried using benchmark FVC 2002 DB1 dataset. Authentication accuracy is found to be nearly 97%. Results compared with state-of-art approaches finds promising. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) that occurs due to insulation breakdown is a precursor to plant failure. PD emits electromagnetic pulses which radiate through space and can be detected using appropriate sensing devices. This paper proposed an enhanced radiolocation technique to locate PD. This approach depends on sensing the radio frequency spectrum and the extraction of PD location features from PD signals. We hypothesize that the statistical characterization of the received PD signals generates many features that represent distinct PD locations within a substation. It is assumed that the waveform of the received signal is altered due to attenuation and distortion during propagation. A methodology for the identification of PD locations based on extracted signal features has been developed using a fingerprint matching algorithm. First, the original extracted signal features are used as inputs to the algorithm. Secondly, Principal Component Analysis (PCA) is used to improve PD localization accuracy by transforming the original extracted features into s new informative feature subspace (principal components) with reduced dimensionality. The few selected PCs are then used as inputs into the algorithm to develop a new PD localization model. This work has established that PCA can provide robust PC representative features with spatially distinctive patterns, a prerequisite for a good fingerprinting localization model. The results indicate that the location of a discharge can be determined from the selected PCs with improved localization accuracy compared to using the original extracted PD features directly. [ABSTRACT FROM AUTHOR]

Fingerprints are one of the most important scientific proof instruments in solving forensic cases. Identification in fingerprints consists of three levels based on the flow direction of the papillary lines at the first level, the minutiae points at the second level, and the pores at the third level. The inadequacy of existing imaging systems in detecting fingerprints and the lack of pore details at the desired level limit the widespread use of third-level identification. The fact that fingerprints with images based on pores in the unsolved database are not subjected to any evaluation criteria and remain in the database reveals the importance of the study to be carried out. In this study, different from classical fingerprint identification methods, a direct pore-based identification system for fingerprint matching is proposed with the dataset created by using the Docucenter Nirvis device and Projectina Image Acquisition-7000 software as a hyperspectral imaging system where pores were visualized more clearly. Although difficult from an operational perspective, the pores in the 800 fingerprints in the database were manually marked for the accuracy of the results. Next, by using a scoring based on iterative closest point algorithm, latent fingerprints were found. Results suggest that the higher the number of pores examined and the more accurately the pores were marked, the higher the hit score. At the same time, query results showed that the scores of other sequential fingerprints in the database which came after the matching fingerprint were even lower. [ABSTRACT FROM AUTHOR]

Unimodal biometric systems rely on a single source or unique individual biological trait for measurement and examination. Fingerprint-based biometric systems are the most common, but they are vulnerable to presentation attacks or spoofing when a fake fingerprint is presented to the sensor. To address this issue, we propose an enhanced biometric system based on a multimodal approach using two types of biological traits. We propose to combine fingerprint and Electrocardiogram (ECG) signals to mitigate spoofing attacks. Specifically, we design a multimodal deep learning architecture that accepts fingerprints and ECG as inputs and fuses the feature vectors using stacking and channel-wise approaches. The feature extraction backbone of the architecture is based on data-efficient transformers. The experimental results demonstrate the promising capabilities of the proposed approach in enhancing the robustness of the system to presentation attacks. [ABSTRACT FROM AUTHOR]

The biometric system relies on a single biometric identifier which could not meet the desired performance required for personal identification. Hence, identification based on the multimodal biometric system is emerged in the research community to achieve the personal identification process more effective. Owing to the strong binding among user identity and biometric template, the user privacy is revealed and hence the security resulted in a major requirement in the biometric system. An authentication based multimodal biometric system is developed in this research by considering different modalities, such as fingerprint, finger vein, and face. Here, the bit string is generated from the biometric sample in such a way that the bit strings are fused by employing the proposed Exponential Water Wave Optimization (EWWO) algorithm based on the involvement of logic operations. However, the process of fusion is accomplished in such a way that it depends on the random selection of two logic operators by the developed optimization approach. Accordingly, the developed EWWO is derived by the combination of Exponentially Weighted Moving Average (EWMA) and Water Wave Optimization (WWO) respectively. The authentication mechanism is achieved by employing the biometric template with the encoder and decoder operation. Moreover, the proposed method achieved the performance for Equal Error rate (EER), False Acceptance Rate (FAR), and False Rejection Rate (FRR) with the value of 0.0717, 0.0745, and 0.0689, respectively. [ABSTRACT FROM AUTHOR]

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has been shown to enhance fingermark recovery compared to standard processes used by police forces, but there is no data to show how generally applicable the improvement is. Additionally, ToF-SIMS can be run in either positive or negative ion mode (or both), and there is no data on which mode of operation is most effective at revealing fingerprints. This study aims to fill these gaps by using ToF-SIMS to image fingerprints deposited on two common exhibit-type surfaces (polyethylene and stainless steel) using 10 donors and ageing fingerprints in either ambient, rainwater, or underground for 1 and 5 months. In all, 120 fingerprints were imaged using ToF-SIMS, and each was run in positive and negative modes. A fingerprint expert compared the fingerprint ridge detail produced by the standard process to the ToF-SIMS images. In over 50% of the samples, ToF-SIMS was shown to improve fingerprint ridge detail visualised by the respective standard process for all surfaces tested. In over 90% of the samples, the ridge detail produced by ToF-SIMS was equivalent to standard development across all different ageing and exposure conditions. The data shows that there is a benefit to running the ToF-SIMS in both positive and negative modes, even if no ridge detail was seen in one mode. [ABSTRACT FROM AUTHOR]

Introduction: Criminals' propensity to precautionary mask their tracks such as disposing of an item of evidence in an aquatic environment (including freshwater and seawater) warrants the ongoing quest for advanced reagents and techniques that are tailored to the substrate characteristics on which the print will be developed. Aim: The study aimed to evaluate the synergic efficiency of the superglue fuming technique (using a homemade fuming chamber) and the powder technique in the development and visualization of latent fingerprints (LFPs) from different nonporous substrates submerged in an aquatic environment for up to 120 days. Materials and Methods: All selected nonporous substrates were cleaned thoroughly with a 70% alcohol swab. Natural latent prints were obtained from a single donor and the substrates were exposed to an aquatic environment. The superglue fuming (using a homemade fuming chamber) plus the powder technique was employed to develop and visualize the print. Results: Our findings disclose that the use of the superglue fuming technique together with the powder technique enhances the recovery rate of LFPs from substrates kept underwater for up to 90 days. The development of fingerprints was challenging from the substrates that were submerged in water for exactly 120 days. Conclusions: The resulting efficiency of the combined technique (superglue fuming technique using a homemade fuming chamber + powder technique) is substantially more sensitive, and selective, and performs better than the previously used stand-alone fingerprint enhancement technique. The improvised homemade fuming chamber proves to be a viable alternative to the standardized fuming cabinet in terms of functionality and sensitivity. [ABSTRACT FROM AUTHOR]

Background: Dactyloscopy is a branch of dermatoglyphics (the study of epidermal ridges) which involves assessment and classification of fingerprint patterns for identification. All fingerprint patterns are laid down permanently from the third month of the intra-uterine life and they remain unchanged throughout the life. This is also the time when all organs in the body are finalizing their development. Therefore, a positive association of the dermatoglyphic features with different diseases like diabetes, mongolism, schizophrenia and leprosy have been well documented in the literature. The aim of the present study was to evaluate the dermatoglyphic patterns and the specific variations which may be used as a valuable diagnostic tool for early detection of diabetes mellitus. Methods: This was a prospective cross-sectional study, a total of 150 diabetic subjects and 150 non-diabetics (control) were selected from the KIUTH Western Uganda and their fingerprints were taken by the Indian ink method. The print patterns including whorls, arches, ulnar and radial loops were analysed and the variations were cross-tabulated between sex, the side of the hand and pattern distribution indexes. Data was analysed using SPSS version 26 and Dankmeijer's index and Furuhata's index were used to assess variability of the fingerprint patterns. Chi square tests was used to test for significance in variations at a confidence interval of 95%. Results and conclusion: Ulnar loops were the most predominant patterns among diabetic subjects at 35.2% followed closely by whorls at 35.1%. The most common pattern were whorls found in both hands of females at 21% (315/1500), whilst the most common pattern for male group were whorls at 17.5% (262). However, some fingers such as the ring finger completely lacked an arch pattern in both groups. The increase in arches was strongly supported by a significant increase in Dankmeijer's index. The presence of high number of ulnar loops can therefore maybe used as an early indicator of type two diabetes. [ABSTRACT FROM AUTHOR]

For any person, company or institution, to secure goods, data, information, different methods were sought and thus the idea of access control appeared. Access control can be done by mobile phone, PIN code, access cards and, perhaps, the most secure way, the biometric feature. Once this idea appeared, it was also desired to automate access, in addition to securing it, and thus, the concept of an access control system appeared. These systems are used on a large scale, both in the public and in the private sector, because they ensure the improvement of the level of protection. This work aims to present a fingerprint-based access control system and the steps that are the basis of its implementation, starting from the block diagram, the functional specifications, the electrical diagram and up to the improvements made to the microcontroller program in the system component. [ABSTRACT FROM AUTHOR]

The employment of various technologies for secured human authentication in the recent years has led to rapid expansion of biometric-based recognition. Among all biometrics, the fingerprint recognition systems (FRS) hold the largest market share and have been used in numerous computing applications such as forensic, governance, and securing critical infrastructure. Though biometric systems provide plentiful benefits over the traditional identification systems but these are also susceptible to various adversarial attacks. The most crucial among all the attacks involves security breaches of stored templates in a central database that may either degrade the overall performance or result in a complete failure of the FRS. To countermeasure these attacks, template protection mechanisms are designed that mitigate the masquerade attacks or template thefts. In this study, we present a novel cancellable approach for fingerprint template protection (FinCaT) using the novel notion of quadrant mapping via a non-invertible transformation function. Our method transforms original fingerprint templates to highly secured templates by using quadrant mapping that maps minutia points by using distinct parameters in each quadrant. The FinCAT approach yields high revocability and diversity as the compromised template of a genuine user can be replaced with a newly generated secured template. The approach is experimentally evaluated on the FVC 2002 fingerprint benchmark dataset. Our technique demonstrates decent performance in terms of an equal error rate (EER) of 5.95% and a recognition accuracy of 94.05%, which is promising as compared to similar state-of-the art template protection techniques. [ABSTRACT FROM AUTHOR]

Fingerprint clusters recognize fingerprint images faster by minimizing the database search space. To this end, the Gaussian filter algorithm is first used to improve the image contrast and calculate the directional fields of the fingerprint, and then the K-means algorithm is used twice, the first time, it is used to classify the fingerprint in terms of the length and width of the fingerprint, and the age group it belongs to, the second time, it is used to classify the fingerprint in terms of to which pattern the direction of ridgelines belong, plain arch, tented arch, ulnar loop, central pocket loop, plain whorl, and double whorl. Due to the smaller search space in the database, good quality results and faster fingerprint recognition are produced. [ABSTRACT FROM AUTHOR]

Multimodal biometrics is the technique of using multiple modalities on a single system. This allows us to overcome the limitations of unimodal systems, such as the inability to acquire data from certain individuals or intentional fraud, while improving recognition performance. In this paper, a study of score normalization and its impact on the performance of the system is performed. The fusion of scores requires prior normalisation before applying a weighted sum fusion that separates impostor and genuine scores into a common interval with close ranges. The experiments were carried out on three biometric databases. The results show that the proposed strategy performs very encouragingly, especially in combination with Empirical Modal Decomposition (EMD). The proposed fusion system shows good performance. The best result is obtained by merging the globality online signature and fingerprint where an EER of 1.69 % is obtained by normalizing the scores according to the Min-Max method. [ABSTRACT FROM AUTHOR]

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Authentication systems have evolved immensely ever since the use of biometrics in the field of security has started. Nonetheless, extensive usage of biometric systems has also resulted in growing fear of identity theft as storing biometric user templates in databases opens up severe security concerns. Consequently, the scope of biometrics invariably depends on the ability of the system to manifest security and robustness against biometric identity theft along with achieving acceptable recognition performance. This paper proposes a user template protection technique to secure a fingerprint based user template used in a biometric authentication system. It is based on the fingerprint shell, which computes alignment-free, singular point independent, and non-invertible user templates. The secure user template generated by the proposed technique comprises fingerprint shell curves computed using the transformed pair-polar structure of the minutia points. The proposed technique has been evaluated on five fingerprint databases of Fingerprint Verification Competitions, namely, FVC2002, FVC2004, and FVC2006. The effectiveness of the technique is analyzed in terms of revocability, diversity, security, and recognition performance. The comparison of results with that of the existing techniques demonstrates the robustness and efficacy of the proposed technique. [ABSTRACT FROM AUTHOR]

This study aimed to enhance the stability and security of power plant control network systems by developing detectable models using artificial intelligence machine learning techniques. Due to the closed system operation policy of facility manufacturers, it is challenging to detect and respond to security threats using standard security systems. With the increasing digitization of control systems, the risk of external malware penetration is also on the rise. To address this, machine learning techniques were applied to extract patterns from network traffic data produced at an average of 6.5 TB per month, and fingerprinting was used to detect unregistered terminals accessing the control network. By setting a threshold between transmission amounts and attempts using one month of data, an anomaly judgment model was learned to define patterns of data communication between the origin and destination. The hypothesis was tested using machine learning techniques if a new pattern occurred and no traffic occurred. The study confirmed that this method can be applied to not only plant control systems but also closed-structured control networks, where availability is critical, and other industries that use large amounts of traffic data. Experimental results showed that the proposed model outperformed existing models in terms of detection efficiency and processing time. [ABSTRACT FROM AUTHOR]

The majority of conventional techniques for latent fingerprint enhancement are based on chemical reactions or interactions between the developer and fingermark residue components, providing contrast between the surface and the fingerprint ridges. However, these methods might be limited by factors associated with particle size, toxicity of the reagent, selectivity, or sensitivity, that may be circumvented by the synthetic design of innovative materials based on nanoparticles or macromolecular materials, such as conducting polymers, applied as latent fingerprint developers on different surfaces and conditions. Since the structural, optical and electronic properties of the material can be tailored by its molecular scale, the size control of the developer plays an important role to improve the interaction with the latent fingermark residue and/or the surface, enhancing the color contrast and the quality of the developed image (or dactylogram). Hence, creating new reagents and strategies to apply nano‐ and macromolecular scale materials as latent fingermark developers constitutes an exciting rapidly expanding area, generating a wealth of new materials that lead to forensic methodologies with enhanced performance. Recent progress in the application of such materials for development of latent fingermarks present on different surfaces and subject to complex circumstances will be discussed. This will focus on the main barriers to making nanoparticles and conducting polymers viable—and occasionally preferred—materials routinely used by forensic experts, and the potential advantages over conventional methodologies in the acquisition and analysis of crime scene evidence. This article is categorized under:Forensic Chemistry and Trace Evidence > Trace EvidenceForensic Chemistry and Trace Evidence > Fingermarks and Other MarksForensic Chemistry and Trace Evidence > Emerging Technologies and Methods [ABSTRACT FROM AUTHOR]

This study focused on recalling efficiency analysis of three different learning rules in Hopfield content addressable recurrent network for fingerprint image patterns. DWT and FFT feature extraction methods have been applied individually and combining one after another to gather the final feature vectors which are then normalized to trained into Hopfiled network by dividing the pattern into four non-overlapping equal sub-regions. To get the relevant features from each sub-region and to reduce the dimensionality of the pattern, DWT is applied first and then followed by FFT feature extraction methods. These feature vectors are normalized to pass into Hopfield network for training with three rules such as Hebbian, Pseudo-inverse and Storkey learning rules. Hamming distance method is used for recalling test pattern with the previously stored patterns. FVC2002 and FVC2004 fingerprint databases are used in simulation process. Performance of the network in terms of recalling efficiency is measured for originally stored patterns and several new patterns by introducing different noise percentages. Results achieved from various observations of this study demonstrated that recalling efficiency in case of DWT plus FFT is outperformed as compared to individual feature extraction techniques as well as work done in the literature. It has been observed that Storkey rule with combined features surpasses other two rules in terms of recalling efficiency. Recalling accuracy is almost 100% for 80, 160 and 240 patterns packing density network for both fingerprint databases used in this study with up to 30% of noise. [ABSTRACT FROM AUTHOR]

Biometric recognition, particularly fingerprint, has been widely adopted in many civil and military applications. However, security concerns have arisen regarding the protection of the saved biometric templates. If the fingerprints database is compromised, the person can no longer use his/her registered fingerprint for authentication. Cancelable biometrics have been used to overcome this issue, by transforming fingerprint templates to a secure representation before saving them on the database. The identity verification is performed in the transformed domain, and a new transform is assigned to a user if his/her biometric data is compromised. In this context, we propose a unique cancelable fingerprint scheme based on the extraction of Scale Invariant Feature Transform (SIFT) features from fingerprint minutiae positions. To provide cancelability, SIFT features are transformed by applying user-specific random projections, followed by quantization to ensure irreversibility. We successfully achieved a zero Equal Error Rate (EER) for the Fingerprint Verification Competition (FVC) 2002 DB1 benchmark in the different-key scenario, and 1.78 EER in the stolen-key scenario. We compare our results with state-of-the-art methods based on the EER metric in the stolen-key scenario. Genuine and impostor distributions have also been used for performance and security assessment of the proposed method. Moreover, we demonstrate the robustness of the proposed approach against brute force attacks. [ABSTRACT FROM AUTHOR]

A motorcycle security system using fingerprint recognition and Telegram notification is a solution to solve motorcycle safety problems and reduce motorcycle loss cases by using fingerprint sensors attached to the owner's motorcycle and Telegram application as a monitor connected to the motorcycle. The microcontroller used is the Wemos D1 mini connected to the Telegram application as a communication line between the user and the motorcycle. The sensor used is the fingerprint sensor. There are differences in data obtained from the fingerprint sensor response results and the response of the Telegram notification system by testing fingerprints that have been registered and that are not registered on the fingerprint sensor with the measurement results using the stopwatch. Measurement differences on the fingerprint sensor have errors with an average time dispute of 0.27 seconds. Measurement differences in Telegram notifications have errors with an average dispute time of 1 second. Hygiene conditions on the fingerprint sensor influence the difference in reading duration. If there is fingerprint oil before it, then the reading tends to be slow. The difference in measurements obtained is small enough that it can be used as a reference. [ABSTRACT FROM AUTHOR]

The most commonly used biometric technique for identifying people is fingerprint-based biometrics. It is divided into two parts: verification (if this individual is genuinely himself) and identification (identifying a person from a pool of persons). Due to the enormous number of comparisons required, the Automatic Fingerprint Identification System (AFIS), which typically conducts two stages: feature extraction and matching, had difficulties with a large database of fingerprint photos for the real-time application. So, more classification stages for complete fingerprint data can make it faster for the AFIS to identify a person. In this paper, we presented a classification method for identifying detailed fingerprint information by utilizing a deep learning approach to support the operations for classifying, identifying, and recognising the fingerprint. The proposed method was designed to differentiate certain fingerprint information, such as left-right hand classification, sweatpore classification, scratch classification, and finger classification. We privately created our fingerprint image dataset due to high personalization and security concerns (25 fingerprint images in the dataset with seven features for each image through the scanning technique). Finally, the research results for the proposed study were accurate and outperformed previous results. [ABSTRACT FROM AUTHOR]

Biometrics is a highly reliable technology where it has become possible to use the characteristics of a person or user (biometrics) along with traditional passwords, and we can even say that it has become an indispensable complement in modern authentication systems today, especially with regard to bank accounts, banks, financial technology (FinTech) internet of things (IoT) devices and all a process related to money and privacy, and biometric methods are multiple and increasing day by day, the most famous of which is (iris, face chart, palm, fingerprint, and others). Biometric systems are immune and immune from modern electronic attacks, such as plagiarism or electronic theft, because the authentication here takes place when all conditions are met. Then the authentication is done and the process is completed, and the aim is to reach the highest levels of accuracy and security and to make the user more comfortable to deal with these modern systems that provide him with many advantages and high privacy. [ABSTRACT FROM AUTHOR]

The existing works on multimodal biometric cues (fingerprint and veins) were found effective for biometric recognition. However, small images size and failure to extract the spatial and temporal information of the existing methods lead to low recognition accuracy. This work proposed spatial and temporal multimodal fingerprint and finger veins network (STMFPFV-Net). In this method, effective image augmentation techniques and large image sizes were utilized as a proxy in the deep convolutional neural network fusion model to improve image variabilities. Recognition was performed, using concatenated layers of a sequential dense network. To evaluate the performance of the proposed method, the NUPT-FPV dataset was used, which contained 16,800 images for fingerprints and finger veins each, respectively. These images were extracted from 6 fingers of 140 participants in both left and right hands, repeated 10 times each. The proposed method when evaluated using standard protocols outperform existing algorithms. [ABSTRACT FROM AUTHOR]

The use of protein stains to enhance fingermark ridge detail in blood is a common technique used by forensic practitioners around the world. Amido Black is one of the most favoured protein stains due to its strong staining ability. The most common formulation of Amido Black is methanol based, with an ability to simultaneously fix and stain the blood impression, however methanol is toxic and can disrupt some surfaces, potentially compromising fingermark detail. If the surface is suspected of being a material that is impacted by methanol, there is an alternative aqueous formulation, which requires a fixative step to set the blood prior to staining so as not to wash away potential ridge detail. The multi-step process of aqueous protein stains is tedious and numerous studies have been conducted to improve the formula to achieve a combined fixing/staining solution that performs like the methanolic reagent. A combined fixative and stain formulation of aqueous based Amido Black was compared to a multi-step formulation with a separate sulfosalicylic acid fixative. Of the 243 split fingermark impressions analysed the majority (63.5 %) showed no preference to either treatment, with a marginally greater proportion of the remaining marks slightly favouring the combined fixative and stain formulation. Given that the new combined formulation performed broadly similarly to the existing multi step formulation, the potential time savings of this simpler approach may be beneficial to implement into operational use. • We compared a combined and separate fixative/stain solution of aqueous Amido Black. • A combined fixative/stain solution for aqueous Amido Black is more efficient. • A combined fixative/stain solution for aqueous Amido Black develops better marks. • Sulfosalicylic Acid as a fixative does not have to be applied as a separate step. • Combining fixative and stain preserves and enhances blood fingermark ridge detail. [ABSTRACT FROM AUTHOR]