Fingerprint Research Articles

Deep blue emission and latent finger print detection analysis of zinc gallate nanoparticles

For the first time, Zinc Gallium Oxide is synthesized using LeucasAspera leaves extract as a reducing agent followed by the calcination at 500∘C. The structural, morphological, and optical properties are discussed. The Bragg reflections confirm that ZGO gets crystallized in pure cubic form. The surface morphology consists of agglomerated NPs. The direct energy band gap was found to be 3.1 eV. The photoluminescence analysis excited under 233 nm, and CIE values fall in the blue region of visible spectra. The CCT value was found to be 2748 K which might find an application as a nanophosphor in warm light-emitting diodes. Further, the powder dusting method was employed to collect the latent fingerprints (LFPs) on the pores and non-pores surfaces under UV as well as in day light. The synthesized ZGO exhibits well-resolved ridge patterns that can be used to identify LFPs with clarity. From all these results, the present synthesized ZGO NPs might find an application in display technology as a blue nanophosphor material and for LFP detection in crime investigation.

Representing the adarsh biometric balak or the ideal biometric child: locating poor children’s care work in the Aadhaar welfare state

ABSTRACT This article spotlights the role of children from poor, underprivileged families who are drawn into care work to facilitate inclusion for their families in food security by performing fingerprint authentication. Owing to the youthful suppleness of their bodies, children’s fingerprints are more likely to succeed in fulfilling biometric requirements. They are also more compliant in having their schooling or playtime disrupted by the unpredictable and time-consuming biometric authentication process. Children’s names may also be added on ration cards to facilitate food security for relatives other than their parents who depend on the biometric viability of children’s bodies and time for welfare. Supplementing ethnographic data with the state’s data of the biometric authentications recorded in the Aadhaar database of India, I make visible the poor child as an ‘ideal biometric child’ in enabling the biometric assemblage of the welfare state to care for its citizens.

Progress towards blue emitting MgO-ZnO-Ga2O3 nanocomposites synthesized by bio mediated route

MgO-ZnO-Ga2O3 nanocomposites are synthesized by solution combustion method using Aloe Vera gel as a reducing agent to increase the efficiency of blue emission. The appearance of Bragg reflections corresponding to MgO, ZnO and Ga2O3 clearly indicates the formation of nanocomposites. The surface morphology consists irregular shape and sized NPs. The Energy dispersive X-ray analysis confirms the purity of the sample. The band energy gap was tuned to 3.1 eV. The Photoluminescence excitation and emission spectra was discussed and compared it with emission spectra of individual oxides as well as with other reported blue emitted nanophosphors. Further, the chromaticity coordinates and Color correlated temperature coordinates clearly confirms their warm blue emission. Further, the powder dusting method was employed to collect the latent fingerprints on the pores and non-pores surfaces. The synthesized MgO-ZnO-Ga2O3 nanocomposites exhibits well-resolved ridge patterns that can be used to identify latent finger prints with clarity. From all these results, the present synthesized MgO-ZnO-Ga2O3 nanocomposite might find an application in display technology as a blue nanophosphor material and for latent finger print detection in crime investigation.

Development and Evaluation of Lavangadi Vati in the Form of Suspension- A Polyherbal Novel Liquid Dosage Form

Introduction: The demand of the herbal medicine is increasing worldwide due to its lesser toxicity and side effects. Though so many formulations are there in ayurveda, the patient compliance for its palatability and acceptability need to consider. The present study focused on the modification of lavangadi vati into suspension, a liquid dosage form, which is effective in prana vaha sroto vikaras (disorders related to respiratory system) and standardising its quality parameters. Aims: To develop lavangadi vati suspension from the classical lavangadi vati dosage form and standardising the process by the evaluation of the micrometric properties, quality control parameters and TLC finger printing. Materials and Method: Among the six trials, the suspension containing lavangadi vati churna in a dose of 1gm/5ml and 4% sodium CMC as the suspending agent with a specific formula and using suitable excipients were selected as master formula. Result: As per the criteria lavangadi vati shown with particle size of 433.7nm, Haussner ratio with 1.06, Carr’s index about 6% and angle of repose with 28. 770.Lavangadi vati suspension shown a pH in the normal range about 6.28 and having high sedimentation range and good redispersibility. Both the vati and modified suspension almost shown similar bands in TLC. Conclusion: After the evaluation of the various quality control parameters, prepared suspension having appropriate properties of a standard suspension without deviating from the classical lavangadi vati formulation.

Synthesis, structure, growth, characterization and quantum chemical studies on 2-amino-5-bromopyridinium trichloroacetate: A novel phasematchable organic NLO single crystal

Organic crystals have become excellent candidates towards nonlinear optical (NLO) applications as they possess superior second and third order optical nonlinearities and high electro-optic coefficients when compared with their inorganic counterparts. In this direction, 2-Amino-5-bromopyridinium trichloroacetate (2A5BPT), a new organic second-order nonlinear optical compound is synthesized and optical grade single crystals are formed utilizing a slow evaporation solution growth approach at room temperature while using methanol as the solvent. Single crystal X-ray diffraction study shows that the title compound is assigned to monoclinic crystal system having non-centrosymmetric space group Cc. The crystal is transparent ( ̴ 85%) throughout the whole visible spectrum, according to the Ultraviolet-Visble (UV–Vis) spectra, with a cutoff wavelength of 367 nm and an optical energy band gap Eg of 3.32 eV. The crystal possesses good thermal stability. The compound has a 1.6 times greater powder second harmonic generation (SHG) efficiency than potassium dihydrogen phosphate (KDP). The phase matchability of the 2A5BPT is studied through particle size dependence of SHG. The intermolecular connection demonstrated by numerous inter contacts has been shown by a molecular Hirshfeld surface analysis of the crystal structure, and the related 2-dimensional finger print analysis further evaluates the individual contribution from each atom. In order to examine the first order hyperpolarizability, molecular electrostatic potential, and Highest occupied molecular orbital-Lowest unoccupied molecular orbital (HOMO-LUMO) energies, the quantum chemical calculations for the title molecule (2A5BPT) were carried out by density functional theory (DFT) utilizing the B3LYP/6–311++G(d,p) basis set. According to these results, the 2A5BPT crystal could be suitable for optoelectronic applications.

Descriptor generation from Morgan fingerprint using persistent homology

ABSTRACT In cheminformatics, molecular fingerprints (FPs) are used in various tasks such as regression and classification. However, predictive models often underutilize Morgan FP for regression and related tasks in machine learning. This study introduced descriptors derived from reshaped Morgan FPs using persistent homology for the predictive accuracy improvement. In the solvation free energy (FreeSolv) and water solubility (ESOL) datasets, persistent homology was found to enhance predictive accuracy compared to the use of only Morgan FPs. Notably, using the first-order persistence diagram (PD1) for descriptor generation resulted in more significant improvements than using the zeroth-order persistence diagram (PD0). Combining 4096 bits Morgan FPs with PD1-generated descriptors increased the average coefficient of determination in the Gaussian process regression from 0.597 to 0.667 for FreeSolv and from 0.629 to 0.654 for ESOL. Adjusting the grid size parameter during PD-based descriptor generation is crucial, as finer grids, especially with PD0, generate more descriptors but reduce predictive accuracy. Coarsening the grid or applying principal component analysis (PCA) mitigates overfitting and enhances accuracy. When descriptors were generated from Morgan FPs with randomly shuffled bit positions, coarsening the grid and/or applying PCA achieved similar accuracy improvements as when the persistent homology of the original Morgan FPs was used.

Multi-Instance Contingent Fusion for the Verification of Infant Fingerprints

It is imperative to establish an automated system for the identification of neonates (1–28 days old) and infants (29 days–12 months old) through the utilisation of the readily accessible 500 ppi fingerprint reader. This measure is crucial in addressing the issue of newborn swapping, facilitating the identification of missing children, monitoring immunisation records, maintaining comprehensive medical history, and other related purposes. The objective of this study is to demonstrate the potential for future identification of infants using fingerprints obtained from a 500 ppi fingerprint reader by employing a fusion technique that combines multiple instances of fingerprints, specifically the left thumb and right index fingers. The fingerprints were acquired from babies who were between the ages of one day and six months at the enrolment session. The sum-score fusion algorithm was implemented. The approach mentioned above yielded verification accuracies of 73.8%, 69.05%, and 57.14% for time intervals of 1 month, 3 months, and 6 months, respectively, between the enrolment and query fingerprints.

Magical fluorescent powder and ink for instantaneous imaging of latent fingerprints (level 1–3) and anti-counterfeiting labels

In the fingerprint research area, development of ‘old’ fingerprints (FPs) or FPs taken on porous, smooth surfaces, preservation of developed FPs and visualization of sweat pores are unsolved issues. In this report, we have synthesized and explored 4,5-bis(diethylene glycol) functionalized 1,8-naphthalimide (Naph-DG) dye for addressing these issues. The Naph-DG possesses high quantum yield (φ = 4.22) in solid-state suitable for the development of latent, old, stained FPs using powder dusting method on porous, non-porous and coloured surfaces up to levels 1–3. Depending upon the hydrophobicity of the surface, Naph-DG Chameleon emits blue, blue-greenish and greenish fluorescence on the ridges of the FPs. The practical applicability of Naph-DG was demonstrated using case studies. The FPs developed with Naph-DG can be lifted with scotch tape for preservation. The Naph-DG was also used (i) as a fluorescent ink for the development of anti-counterfeiting labels and (ii) for molecular recognition of Fe3+ as low as 42 nM in CH3CN.

XAI-driven digital twin for cobot dynamic error compensation

Process and product fingerprints (FP) have been approved as effective parameters to reveal the principal contributing factors towards functionality in smart manufacturing processes. Though AI-driven methods outperform other approaches for fingerprint extraction, the lack of explainability in its black-box style predictions leads to misconceptions and trust issues among stakeholders. In this study, a novel explainable-AI (XAI) approach is proposed to identify mathematical fingerprint expressions by formulating them as graphs using the QLattice algorithm, inspired by path integral formulation. Here, the Qlattice model identifies explainable and human-comprehensible FP expressions for cobot dynamic error based on accelerometer signal features. The discovered symbolic model is subsequently applied to a digital twin which successfully tracked and compensated for dynamic errors autonomously in real time.

Deep Cancelable Multibiometric Finger Vein and Fingerprint Authentication With Non-Negative Matrix Factorization

Deep Cancelable Multibiometric Finger Vein and Fingerprint Authentication With Non-Negative Matrix Factorization

An Interpretable Siamese Attention Res‐CNN for Fingerprint Spoofing Detection

In recent years, fingerprint authentication has gained widespread adoption in diverse identification systems, including smartphones, wearable devices, and attendance machines, etc. Nonetheless, these systems are vulnerable to spoofing attacks from suspicious fingerprints, posing significant risks to privacy. Consequently, a fingerprint presentation attack detection (PAD) strategy is proposed to ensure the security of these systems. Most of the previous work concentrated on how to build a deep learning framework to improve the PAD performance by augmenting fingerprint samples, and little attention has been paid to the fundamental difference between live and fake fingerprints to optimize feature extractors. This paper proposes a new fingerprint liveness detection method based on Siamese attention residual convolutional neural network (Res‐CNN) that offers an interpretative perspective to this challenge. To leverage the variance in ridge continuity features (RCFs) between live and fake fingerprints, a Gabor filter is utilized to enhance the texture details of the fingerprint ridges, followed by the construction of an attention Res‐CNN model to extract RCF between the live and fake fingerprints. The model mitigates the performance deterioration caused by gradient disappearance. Furthermore, to highlight the difference in RCF, a Siamese attention residual network is devised, and the ridge continuity amplification loss function is designed to optimize the training process. Ultimately, the RCF parameters are transferred to the model, and transfer learning is utilized to aid its acquisition, thereby assuring the model’s interpretability. The experimental outcomes conducted on three publicly accessible fingerprint datasets demonstrate the superiority of the proposed method, exhibiting remarkable performance in both true detection rate and average classification error rate. Moreover, our method exhibits remarkable capabilities in PAD tasks, including cross‐material experiments and cross‐sensor experiments. Additionally, we leverage Gradient‐weighted Class Activation Mapping to generate a heatmap that visualizes the interpretability of our model, offering a compelling visual validation.

Dermatoglyphic Patterns in Undergraduate Medical Students and their Association with Academic Performance: A Cross-sectional Study

Introduction: Dermatoglyphics is the study of fingerprint patterns, which are unique to each individual and remain unchanged after birth. It has been used in criminology for decades. In recent years, its role in screening various medical conditions has been established. Dermatoglyphics has also been associated with cognitive ability, making it a potential predictor of academic potential. Aim: To investigate the association between dermatoglyphic patterns and the academic performance of medical students. Materials and Methods: A cross-sectional study was conducted in the Department of Anatomy, Jawaharlal Nehru Medical College, Wardha, Maharashtra, India on 200 undergraduate medical students (94 males and 106 females) from January 2019 to December 2020. Fingerprint patterns were obtained using the standard ink method. The parameters studied included arches, loops, whorls, composites, Total Finger Ridge Count (TFRC), and the 'atd' angle. The academic performance of participants was assessed based on the marks obtained in the National Eligibility Cum Entrance Test (NEET) and the overall marks scored in the first-year university examination. Statistical analysis involved the use of arithmetic mean, standard deviation, Chi-square test, and one-way Analysis of Variance (ANOVA). Results: In present study, the most common fingerprint pattern in the right hand was an arch (30.9%), followed by a whorl (24.5%). In the left hand, the most common fingerprint pattern was a whorl (40.6%), followed by an ulnar loop (20.1%), with no gender difference observed. Thumb, index finger, middle finger, ring finger, and little finger exhibited different fingerprint patterns between the right and left hand, indicating asymmetry. This asymmetry was associated with lower academic performance. Higher academic performers in the NEET had a higher frequency of whorls and composites. TFRC showed no association with academic performance. Students with an ‘atd’ angle between 41 and 50° demonstrated higher academic performance. Conclusion: The present study found that asymmetry of fingerprint patterns and a higher ‘atd’ angle (≥51°) were correlated with lower academic performance among medical students. These findings suggest potential directions for early academic intervention, provided multicentric studies are conducted in the future.

DeepDIST: A Black-Box Anti-Collusion Framework for Secure Distribution of Deep Models

Due to enormous computing and storage overhead for well-trained Deep Neural Network (DNN) models, protecting the intellectual property of model owners is a pressing need. As the commercialization of deep models is becoming increasingly popular, the pre-trained models delivered to users may suffer from being illegally copied, redistributed, or abused. In this paper, we propose DeepDIST, the first end-to-end secure DNNs distribution framework in a black-box scenario. Specifically, our framework adopts a dual-level fingerprint (FP) mechanism to provide reliable ownership verification, and proposes two equivalent transformations that can resist collusion attacks, plus a newly designed similarity loss term to improve the security of the transformations. Unlike the existing passive defense schemes that detect colluding participants, we introduce an active defense strategy, namely damaging the performance of the model after the malicious collusion. The extensive experimental results show that DeepDIST can maintain the accuracy of the host DNN after embedding fingerprint conducted for true traitor tracing, and is robust against several popular model modifications. Furthermore, the anti-collusion effect is evaluated on two typical classification tasks (10-class and 100-class), and the proposed DeepDIST can drop the prediction accuracy of the collusion model to 10% and 1% (random guess), respectively.

MotoPrint: Reconfigurable Vibration Motor Fingerprint via Homologous Signals Learning

Device fingerprints can satisfy the high-security requirement of modern mobile applications (e.g., mobile payments) by guaranteeing the operation is performed on a trusted device. However, existing works on device fingerprints are weak to leakage, which leads to an irreversible failure of the device fingerprint authentication system after suffering from fingerprint theft attacks. The vulnerability drives us to propose a reconfigurable device fingerprint, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> , that can recover the system after suffering from such attacks. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> stems from the motor vibration that can represent in both signals of the accelerometer and the gyroscope (i.e., they are homologous motion signals). Therefore, we designed a two-path feature extracting network and a sensor-independent training strategy to eliminate sensor noise that can decline authentication performance. In addition, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> has a complete reconfiguration mechanism to cope with fingerprint leakage, which brings the damaged authentication system back to health. The evaluation of 80 stand-alone vibration motors and 20 in-built ones shows that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> can achieve high authentication accuracy of 98.5%. Meanwhile, we also demonstrate the reconfigured <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> , which can also effectively indicate the device's uniqueness with over 98% accuracy, is independent of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> s under other stimulating codes.

Dermatoglyphics: The elusive nexus decoding the conundrum of amlodipine-induced gingival enlargement – A cross-sectional, pilot, observational study

Abstract: Background: Dermatoglyphics is a scientific study dealing with the dermal ridge configurations of the palmar and plantar surfaces of the upper and lower limbs. Drug-induced gingival enlargement refers to engorgements in gingiva as a consequence of drug usage. This pilot study was done to see the pattern of fingerprints using dermatoglyphics and amlodipine-induced gingival enlargement in hypertensive patients. Materials and Methods: The subjects for the study were prospectively recruited from the outpatients reporting to the Department of Periodontology, Tamil Nadu Government Dental College during the study period of 3 months following the inclusion criteria. Twenty people presenting with amlodipine induced gingival enlargement were thus recruited for the study and their fingerprint patterns were recorded and analyzed with the assistance of magnifying lens. Results: It was thus observed that there was a preponderance of whorls (55%) while observing fingerprint patterns of the recipients of amlodipine presenting with gingival enlargement. Females outnumbered males in the study. The gender difference was seen in the type of fingerprint. A preponderance of whorls (55%, 11 participants) 30% arch (6 participants) and 15% with loop (3 participants). Similar results were recorded in both genders with whorl recording the highest incidence of 80% and 46.67% in males and females, respectively. No zero loop patterns were recorded in males. Conclusion: The relative preponderance of whorls being observed in this study might be a predictive marker for those amlodipine consumers with a heightened predilection to develop gingival enlargement The subdued expression of loops which are otherwise the most commonly expressed fingerprint pattern among the humans, and the ascendancy of whorls characterizing this category of people is indeed a finding, which by further supportive studies, could help find preventive strategies to combat amlodipine-induced gingival enlargement.

Fingerprint Examination by Gender in Healthy Turkish Population

Abstract Background: Dermatoglyphic forensic institutions provide important information that can also be used in the field of anthropology and medicine. To expand the existing data of the Turkish society and increasing literature data on fingerprint. Materials and Methods: This study was conducted with 147 healthy young people. The parameters examined were compared between right and left hands and between genders. The fingerprints were divided into two groups as whorl, loop, arch, and accidental pattern. In addition, “total finger ridge count, absolute finger ridge count, Pattern Intensity Index (PII), Cummins index (CI), Poll index (PI), Dankmeijer index (DI), Volotzkoy index (VI), and Furuhata’s index (FI)” of fingerprints on both hands and right-left hands separately were calculated. Results: In the joint evaluation of 10 fingers in both genders, the most common type was found to be loop pattern. The index data of the Turkish population are as follows (respectively, female and male): PII – 13.2/12.6, CI – 0.1/0.2, PI – 1/1, DI – 11.9/33.3, VI – 132.3/126.7, and FI – 128/133.7. There was no significant difference in the compared parameters between males and females and both hands (P &gt; 0.05). Conclusions: According to the data obtained, the Turkish population data correspond to the Mediterranean subgroup of the European races. Moreover, as seen in this study, ulnar loop density is more common in Eastern countries.

Antimicrobial soap development from leaf extract of Rhinacanthus nasutus

Bacterial skin infections are most prevalent in humans. These skin infections can be easily prevented by the topical application of herbal soaps produced from the medicinal plants. Medicinal plants contain natural bioactive compounds with a variety of biological potential that can be used to incorporate into a basic soap formulation. This study aims to prepare the antibacterial soap using leaf extract of local medicinal plant, Rhinachantus nasutus. The leaf extract was prepared by maceration extraction using methanol as solvent. The antibacterial and antifungal activities of the leaf extract and herbal soap were evaluated using the disc diffusion assay against Bacillus cereus, Bacillus subtilis, Staphylococcus aureus, Escherichia coli, Yersinia enterocolitica, Methicillin-resistant Staphylococcus aureus (MRSA), Aspergillus niger and Candida albicans. The thump impression test was employed to test the effectiveness of the soap as antibacterial and antifungal soap. The results obtained from the current study revealed that the leaf extract of R. nasutus efficiently inhibited the growth of all test microorganisms at a concentration of 25 mg/mL with a diameter of inhibition zone ranging from 1.0 ± 0.1 mm to 2.5 ±0.1 mm, whereas a prepared soap with a similar concentration also inhibited all test microorganisms with an inhibition zone ranging from 0.7±0.1 mm to 1.3±0.1 mm. A thumb impression test result indicated a reduction in microbial development when using herbal soap. Leaf extracts of R.nasutus can be used to produce herbal soaps with remarkable antibacterial activity.

Phytochemical and Chromatographic Studies in the Latex Milk Powder of P. Daemia. Linn

India being a rich and varied flora of medicinal plants since the Vedic period. The present study deals with the scientific validation of P. daemia.Linn with special reference to its pharmacognostical and phytochemical investigations. This is commonly known as P. daemia, figure and is known to have medicinal properties, also even used as home remedies in the rural and the remotest parts of the India Qualitative &amp; Quantitative analysis for nutritional value and amino acid detection for both sample has been estimated &amp; detected TLC finger prints of latex of at 366 nm (before derivatization) &amp; Rf values in TLC finger prints of latex at 254 nm (after derivatization) has been studied for both Samples. Latex is applied externally on chronic infected wounds to alleviate edema, pain and to promote the healing; therefore an attempt has been made to carried out the detailed quality control and assurance of the drug followed by HPTLC profiles, HPTLC profile of P. Daemia latex milk powder observed under 366 nm &amp; 254 nm shows the variety of results A – C (Gautala forest,) B - Aurangabad (M.S.) Sample A, B &amp; C.

An Empirical Approach to Evaluate Employee Performance Using Finger Print Attendance

An Empirical Approach to Evaluate Employee Performance Using Finger Print Attendance

On the Performance of Metamaterial based Printed Circuit Antenna for Blood Glucose Level Sensing Applications: A Case Study

Due to the urgent need to develop technologies for continuous glucose monitoring in diabetes individuals, poten tial research has been applied by invoking the microwave tech niques. Therefore, this work presents a novel technique based on a single port microwave circuit, antenna structure, based on Metamaterial (MTM) transmission line defected patch for sensing the blood glucose level in noninvasive process. For that, the proposed antenna is invoked to measure the blood glu cose through the field leakages penetrated to the human blood through the skin. The proposed sensor is constructed from a closed loop connected to an interdigital capacitor to magnify the electric field fringing at the patch center. The proposed an tenna sensor is found to operate excellently at the first mode, 0.6GHz, with S11 impedance matching less than -10dB. The proposed sensor performance is tested experimentally with 15 cases, different patients, through measuring the change in the S11 spectra after direct touching to the sensor a finger print of a patient. The proposed sensor is found to be effectively very efficient detector for blood glucose variation with a low manu facturing cost when printed on an FR-4 substrate. The experi mental measurements are analyzed mathematically to obtain the calibration equation of the sensor from the curve fitting. Finally, the theoretical and the experimental results are found to be agreed very well with a percentage of error less than 10%.