Efficient Fingerprint Research Articles

Quaternary phosphonium AIEgens nanoparticles as innovative agents for developing latent fingerprints

Quaternary phosphonium salts, a significant category of organophosphorus compounds, have garnered substantial attention from chemists due to their wide range of applications across various research areas. These compounds are utilized in organic synthesis, catalysis, medicinal chemistry, natural materials, and coordination chemistry. Their versatility and effectiveness in these fields make them valuable tools in scientific research. Despite their extensive use in various applications, the potential of quaternary phosphonium compounds as fluorescent agents for revealing latent fingerprints (LFPs) remains largely unexplored, presenting an exciting opportunity for further research and development in forensic science. In this study, we designed molecules that combine the aggregation-induced emission (AIE) chromophore with triphenylphosphine to create a series of novel AIE amphiphiles, namely TPP1, TPP2, and TPP3. Through precise adjustment of the carbon chain length between the phenoxy group and the terminal triphenylphosphine, we were able to finely tune the nanostructures and hydrophobicity of the materials. TPP3 emerged as the optimal candidate, possessing the ideal particle size and hydrophobicity to effectively bind to LFPs, thus enabling efficient fingerprint visualization with enhanced fluorescence upon aggregation. Our findings introduce an innovative approach to fingerprint visualization, offering high selectivity, superior imaging of level 3 structures, and long-term effectiveness (up to 30 days). Additionally, TPP3's outstanding performance in imaging level 3 structures of LFPs is beneficial for analyzing incomplete LFPs and identifying individuals. By significantly improving the detection and analysis of LFPs, this approach ensures more accurate and reliable identification, making it invaluable for forensic investigations and security measures. The adaptability of these compounds to various fingerprint surfaces highlights their potential in diverse practical applications, enhancing their utility in both forensic science and security fields. This versatility allows for precise fingerprint visualization across different scenarios, making them a critical tool for advancing biometric and security technologies.

Establishing a fingerprinting method for fast catheter identification in HDR brachytherapy in vivo dosimetry

To use quantities measurable during in vivo dosimetry to build unique channel identifiers, that enable detection of brachytherapy errors. Treatment plan of 360 patients with prostate cancer who underwent high-dose-rate brachytherapy (range, 16-25 catheters; mean, 17) were used. A single point virtual dosimeter was placed at multiple positions within the treatment geometry, and the source-dosimeter distance and dwell time were determined for each dwell position in each catheter. These values were compared across all catheters, dwell position by dwell position, simulating a treatment delivery. A catheter was considered uniquely identified if, for a given dwell position, no other catheters had the same measured values. The minimum number of dwell positions needed to identify a specific catheter and the optimal dosimeter location uniquely were determined. The radial (r) and vertical (z) dimensions of the source-dosimeter distance were also examined for their utility in discriminating catheters. Using a virtual dosimeter with no uncertainties, all catheters were identified in 359 of the 360 cases with 9 dwell position measurements. When only the dwell time were measured, all catheters were uniquely identified after 1 dwell position. With a 2-mm spatial accuracy (r,z), all catheters were identified in 94% of the plans. Simultaneous measurement of source-dosimeter distance and dwell time ensured full catheter identification in all plans ranging from 2 to 6 dwell positions. The number of dwell positions needed to uniquely identify all catheters was lower when the distance from the implant center was higher. The most efficient fingerprinting approach involved combining source-dosimeter distance (i.e., source tracking) and dwell time. The further the dosimeter is placed from the center of the implant the better it can uniquely identify catheters.

Development of a preliminary extraction protocol for phenol compounds during table grape grafting formation

The development of rootstocks that are less sensitive to abiotic and biotic stresses can help mitigate the negative effects of climate change on crop productivity, soil health, and water use. Though, the phenomenon of graft incompatibility is a significant limitation to the spread of new rootstock genotypes. Numerous studies have focused on this issue, highlighting the role of certain phenolic molecules as predictive markers of incompatibility. Given the lack of specific research on table grape, , this study proposes a protocol for the extraction of polyphenols from its woody tissues, which is a fundamental prerequisite for further analysis on secondary metabolites involved in graft incompatibility. The proposed eco-friendly method coupled a traditional maceration using solvent with a green technique utilizing an ultrasound-assisted extractor. The following parameters were compared: (1) sample weight (0.1 g, 0.5 g, and 1 g), (2) time of ultrasound-assisted extraction (10 min, 20 min, and 30 min), and (3) solvent volume for maceration (10 mL, 15 mL, and 20 mL). Four phenol classes were considered based on previous works on <i>Vitis</i> spp.: cinnamic acids, flavonols, benzoic acids, and catechins. The characterization of polyphenolic biomarkers was carried out via HPLC. 1 g of plant material, 30 minutes of sonification, and 20 mL of organic solvent was the combination of factors that resulted in the most efficient fingerprint, both quantitatively (267.68 ± 3.91 mg/100 g fresh weight [FW]) and qualitatively, with the four classes analyzed significantly represented. This is the first work to come up with an extraction protocol for phenol compounds in table grape woody tissue based on both ecological and routine techniques.

Phase-Aggregated Dual-Branch Network for Efficient Fingerprint Dense Registration

Phase-Aggregated Dual-Branch Network for Efficient Fingerprint Dense Registration

Sweep-to-Unlock: Fingerprinting Smartphones Based on Loudspeaker Roll-Off Characteristics

Fingerprinting smartphones based on acoustic characteristics of their loudspeaker may have a number of applications in device-to-device authentication as well as in forensic investigations. In this work we propose an efficient fingerprinting methodology by using the roll-off characteristics of the device speaker, i.e., the transition between the low and high stopbands to the passband segment of the speaker. We extract roll-off characteristics from sweep signals, also know as chirps, that are commonly used in practice to test speaker response. This procedure appears to be more stable against variations of the volume level and allows the use of simple linear approximations, which are intuitive and easy to compute, in order to extract the fingerprint. To increase detection accuracy, on the basis of the proven performance of deep learning techniques, a convolutional and a bi-directional long short term memory neural network are further proposed and their performance demonstrated for authentication purposes. While numerous applications may be envisioned, we specifically focus on the use of speaker characteristics in relation to in-vehicle infotainment units, checking if recordings from these units can be used to fingerprint a specific phone.

Strongly fluorescent conjugated polymer nanoparticles in aqueous colloidal solution for universal, efficient and effective development of sebaceous and blood fingerprints

Taking the same developing strategy for different types of latent fingerprints is helpful in improving the efficiency of criminal investigation. Here we advanced a new strategy based on amino-functionalized poly(p-phenylenevinylene) nanoparticles (PPV-brPEI NPs) in aqueous colloidal solution as the developing reagent. The desirable amino functionality and strong emission of NPs were simultaneously realized by adding branched polyethyleneimine (brPEI) during the process of thermal elimination of the PPV polymer precursor. The NPs were demonstrated to have negligible effects on the extraction of biological information from DNA. Using the PPV-brPEI NPs-soaked cotton pad, both latent sebaceous fingerprints (LSFPs) and latent blood fingerprints (LBFPs) can be effectively developed on different nonporous substrates. This strategy was highly sensitive and effective for aged, contaminated and moldy fingerprints. Additionally, the developed fingerprints could tolerate humidity environment and the alcohol atmosphere. The mechanism investigation suggests that interaction between PPV-brPEI NPs and sebum ingredients contributes to the development of LSFPs and interaction between PPV-brPEI NPs and proteins in blood contributes to the development of LBFPs, but the former is not as stable as the latter. This work provides a simple, environment/operator-friendly strategy for efficient fingerprint development, which is very promising for practical criminal investigations.

Biometric technology for personal identification and authentication: fingerprint

This paper presents an efficient fingerprint verification algorithm that improves matching accuracy. Algorithms for image processing for biometric technologies of identification and authentication of a person using fingerprints are considered. They are essential tools for improving performance across a wide range of industries. The recognition system is designed for storing data, further processing it, identifying and displaying fingerprints. In forensics, modern image processing algorithms are used and provide a significant improvement in work results. Fingerprint images are degraded and damaged due to changes in skin and print conditions. Thus, image enhancement techniques are used before feature points are detected and fine details are extracted. Templates are generated based on feature vector extraction and databases are created for verification and identification of fingerprint images. The method of experimental research is carried out, the process of processing the identification results is considered. To solve this problem, a recognition system was proposed, which is designed to store data, further process it, identify and display fingerprints. They are used in places of high secrecy as a kind of password access key based on scanning and verifying fingerprints with the FVC2002 database. The results obtained show that the proposed algorithm is more efficient and reliable.

RF eigenfingerprints, an Efficient RF Fingerprinting Method in IoT Context.

In IoT networks, authentication of nodes is primordial and RF fingerprinting is one of the candidates as a non-cryptographic method. RF fingerprinting is a physical-layer security method consisting of authenticated wireless devices using their components’ impairments. In this paper, we propose the RF eigenfingerprints method, inspired by face recognition works called eigenfaces. Our method automatically learns important features using singular value decomposition (SVD), selects important ones using Ljung–Box test, and performs authentication based on a statistical model. We also propose simulation, real-world experiment, and FPGA implementation to highlight the performance of the method. Particularly, we propose a novel RF fingerprinting impairments model for simulation. The end of the paper is dedicated to a discussion about good properties of RF fingerprinting in IoT context, giving our method as an example. Indeed, RF eigenfingerprint has interesting properties such as good scalability, low complexity, and high explainability, making it a good candidate for implementation in IoT context.

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform

Predicting postoperative complications has the potential to inform shared decisions regarding the appropriateness of surgical procedures, targeted risk-reduction strategies, and postoperative resource use. Realizing these advantages requires that accurate real-time predictions be integrated with clinical and digital workflows; artificial intelligence predictive analytic platforms using automated electronic health record (EHR) data inputs offer an intriguing possibility for achieving this, but there is a lack of high-level evidence from prospective studies supporting their use. To examine whether the MySurgeryRisk artificial intelligence system has stable predictive performance between development and prospective validation phases and whether it is feasible to provide automated outputs directly to surgeons' mobile devices. In this prognostic study, the platform used automated EHR data inputs and machine learning algorithms to predict postoperative complications and provide predictions to surgeons, previously through a web portal and currently through a mobile device application. All patients 18 years or older who were admitted for any type of inpatient surgical procedure (74 417 total procedures involving 58 236 patients) between June 1, 2014, and September 20, 2020, were included. Models were developed using retrospective data from 52 117 inpatient surgical procedures performed between June 1, 2014, and November 27, 2018. Validation was performed using data from 22 300 inpatient surgical procedures collected prospectively from November 28, 2018, to September 20, 2020. Algorithms for generalized additive models and random forest models were developed and validated using real-time EHR data. Model predictive performance was evaluated primarily using area under the receiver operating characteristic curve (AUROC) values. Among 58 236 total adult patients who received 74 417 major inpatient surgical procedures, the mean (SD) age was 57 (17) years; 29 226 patients (50.2%) were male. Results reported in this article focus primarily on the validation cohort. The validation cohort included 22 300 inpatient surgical procedures involving 19 132 patients (mean [SD] age, 58 [17] years; 9672 [50.6%] male). A total of 2765 patients (14.5%) were Black or African American, 14 777 (77.2%) were White, 1235 (6.5%) were of other races (including American Indian or Alaska Native, Asian, Native Hawaiian or Pacific Islander, and multiracial), and 355 (1.9%) were of unknown race because of missing data; 979 patients (5.1%) were Hispanic, 17 663 (92.3%) were non-Hispanic, and 490 (2.6%) were of unknown ethnicity because of missing data. A greater number of input features was associated with stable or improved model performance. For example, the random forest model trained with 135 input features had the highest AUROC values for predicting acute kidney injury (0.82; 95% CI, 0.82-0.83); cardiovascular complications (0.81; 95% CI, 0.81-0.82); neurological complications, including delirium (0.87; 95% CI, 0.87-0.88); prolonged intensive care unit stay (0.89; 95% CI, 0.88-0.89); prolonged mechanical ventilation (0.91; 95% CI, 0.90-0.91); sepsis (0.86; 95% CI, 0.85-0.87); venous thromboembolism (0.82; 95% CI, 0.81-0.83); wound complications (0.78; 95% CI, 0.78-0.79); 30-day mortality (0.84; 95% CI, 0.82-0.86); and 90-day mortality (0.84; 95% CI, 0.82-0.85), with accuracy similar to surgeons' predictions. Compared with the original web portal, the mobile device application allowed efficient fingerprint login access and loaded data approximately 10 times faster. The application output displayed patient information, risk of postoperative complications, top 3 risk factors for each complication, and patterns of complications for individual surgeons compared with their colleagues. In this study, automated real-time predictions of postoperative complications with mobile device outputs had good performance in clinical settings with prospective validation, matching surgeons' predictive accuracy.

Efficient Wi-Fi Fingerprint Crowdsourcing for Indoor Localization

Wi-Fi Received Signal Strength (RSS, fingerprints) based indoor localization is promising and widely investigated with the pervasive deployment of Wi-Fi Access Points. However, the process to collect RSS, also known as site survey, is labor-intensive. Thus, we propose and demonstrate an efficient fingerprint crowdsourcing method in this paper. Specifically, RSS measurements are obtained and annotated with location tags while a participant is walking along a chosen path with a smartphone at hand. In the localization stage, we adopt the Gaussian Process based solution and propose a novel mean function selection method. Extensive experiments show that the path-based site survey can achieve a comparable localization performance to the point-based site survey, but takes less survey time. We find that fingerprints collected while walking are more suitable for localizing moving pedestrians. In addition, due to the sparsity of fingerprints collected through crowdsourcing, the proposed mean function selection strategy is advantageous and can reduce localization errors significantly compared to a baseline solution.

Latent Fingerprint Indexing: Robust Representation and Adaptive Candidate List

Efficiently identifying the mated gallery fingerprint of a latent fingerprint in a large database requires a highly accurate and efficient fingerprint matching algorithm. The common strategy to achieve this goal is to combine an efficient indexing algorithm with a slow but accurate matching algorithm. Despite of the importance of latent indexing, it has received far less attention than rolled and plain fingerprint indexing. Due to the small fingerprint area, poor image quality and huge variety in information quantity of latent fingerprints, existing rolled and plain fingerprint indexing approaches cannot be simply migrated to the latent fingerprint indexing. In this paper, we propose (1) a multi-scale fixed-length representation approach for latent fingerprint indexing, and (2) a fingerprint information quantity estimation approach for adaptive candidate list reduction. The representation scheme is designed to deal with small finger area and low image quality of latents. The information quantity of a latent is a predictor of the indexing score of its mated gallery fingerprint and thus can be used to determine a proper threshold for its candidate list. Extensive experimental results on NIST SD27, MOLF, N2N, and Hisign latent fingerprint databases show that the proposed method achieved the state-of-the-art indexing accuracy on latent fingerprints, and significantly improved the efficiency of state-of-the-art latent matching algorithm.

A robust and efficient fingerprint minutiae extraction in post-processing algorithm

A robust and efficient fingerprint minutiae extraction in post-processing algorithm

A robust and efficient fingerprint minutiae extraction in post-processing algorithm

A robust and efficient fingerprint minutiae extraction in post-processing algorithm

Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications

In this work, the Discrete, Environment-Driven Ray Launching (DED-RL) algorithm, which makes use of parallelization on Graphic Processing Units, fully described in a previous paper, has been validated versus a large set of measurements to evaluate its performance in terms of both computational efficiency and accuracy. Three major urban areas have been considered, including a very challenging scenario in central San Francisco that was used as a benchmark to test an image-ray tracing algorithm in a previous work. Results show that DED-RL is as accurate as ray tracing, despite the much lower computation time, reduced by more than three orders of magnitude with respect to ray tracing. Moreover, the accuracy level only marginally depends on discretization pixel size, at least for the considered pixel size range. The unprecedented computational efficiency of DED-RL opens the way to numerous applications, ranging from RF coverage optimization of drone-aided cellular networks to efficient fingerprinting localization applications, as briefly discussed in the paper.

A robust and efficient fingerprint image restoration method based on a phase-field model

In this study, we present a robust and efficient fingerprint image restoration algorithm using the nonlocal Cahn–Hilliard (CH) equation, which was proposed for modeling the microphase separation of diblock copolymers. We take a small local region embedding the damaged domain and solve the nonlocal CH equation to restore the fingerprint image. A Gauss–Seidel type iterative method, which is efficient and simple to implement, is used. The proposed method has the advantage in that the pixel values in the damaged fingerprint domain can be obtained using the image information from the outside of the damaged fingerprint region. Fingerprint restoration based on adjacent pixel information can ensure the accuracy of the fingerprint information with a low computational cost. Computational experiments demonstrated the superior performance of the proposed fingerprint restoration algorithm.

Efficient fingerprint features for gender recognition

Efficient fingerprint features for gender recognition

Efficient experimental quantum fingerprinting with channel multiplexing and simultaneous detection

Quantum communication complexity explores the minimum amount of communication required to achieve certain tasks using quantum states. One representative example is quantum fingerprinting, in which the minimum amount of communication could be exponentially smaller than the classical fingerprinting. Here, we propose a quantum fingerprinting protocol where coherent states and channel multiplexing are used, with simultaneous detection of signals carried by multiple channels. Compared with an existing coherent quantum fingerprinting protocol, our protocol could consistently reduce communication time and the amount of communication by orders of magnitude by increasing the number of channels. Our proposed protocol can even beat the classical limit without using superconducting-nanowire single photon detectors. We also report a proof-of-concept experimental demonstration with six wavelength channels to validate the advantage of our protocol in the amount of communication. The experimental results clearly prove that our protocol not only surpasses the best-known classical protocol, but also remarkably outperforms the existing coherent quantum fingerprinting protocol.

FinPrivacy: A Privacy-preserving Mechanism for Fingerprint Identification

Fingerprint provides an extremely convenient way of identification for a wide range of real-life applications owing to its universality, uniqueness, collectability, and invariance. However, digitized fingerprints may reveal the privacy of individuals. Differential privacy is a promising privacy-preserving solution that is enforced by injecting random noise into preserved objects, such that an adversary with arbitrary background knowledge cannot infer private input from the noisy results. This study proposes FinPrivacy, a privacy-preserving mechanism for fingerprint identification. This mechanism utilizes the low-rank matrix approximation to reduce the dimensionality of fingerprint and the exponential mechanism to carefully determine the value of the optimal rank. Thereafter, FinPrivacy injects Laplace noise to the singular values of the approximated singular matrix, thereby trading off between privacy and utility. Analytic proofs and results of the comparative experiments demonstrate that FinPrivacy can simultaneously enforce ɛ-differential privacy and maintain an efficient fingerprint recognition.

Analysis of Finger Print Bank Algorithm based Fingerprint Matching Scheme

Now-a-days it is an important need to preserve one’s data/information securely without giving an option to attackers or intruders to steal it. For this case authentication are considered to be the most important feature, which allows everyone to individually prove their authentication to the system and once it is correct allow them to access the features of the system otherwise neglect or block them to proceed further. There are many authentication schemes available in the information technology and security industries such as Biometric Scheme, IRIS Matching Scheme, Face Recognition, Password Management Scheme and so on. The most popular, well-known and one of the best classical scheme is called Biometric Finger Print Matching Scheme, which allows the user to register the finger print into the system for training purpose and further at every time of accessing the features into the system it gathers the present (testing) finger print from the user and match it with the already registered finger print, once it matches with the trained sample, it allows the user to login into the system or else block the user to proceed further. The proposed Finger Print Bank Algorithm uses an efficient finger print matching principles to accurately match the correct finger print and gives a Boolean result to user to inform that to proceed further or not. The Finger Print Bank Algorithm uses efficient filtering schemes to filter the finger print to extract the internal and global core details of it as well as extracts the raw code of it and compares that with the already registered finger print. For all the entire proposed approach guarantees for the best result and accuracy in results.

Data Deduplication System Based on Content-Defined Chunking Using Bytes Pair Frequency Occurrence

Every second, millions of data are being generated due to the use of emerging technologies. It is very challenging to store and handle such a large amount of data. Data deduplication is a solution for this problem. It is a new technique that eliminates duplicate data and stores only a single copy of data, reducing storage utilization and the cost of maintaining redundant data. Content-defined chunking (CDC) has been playing an important role in data deduplication systems due to its ability to detect high redundancy. In this paper, we focused on deduplication system optimization by tuning relevant factors in CDC to identify chunk cut-points and introduce an efficient fingerprint using a new hash function. We proposed a novel bytes frequency-based chunking (BFBC) algorithm and a new low-cost hashing function. To evaluate the efficiency of the proposed system, extensive experiments were done using two different datasets. In all experiments, the proposed system persistently outperformed the common CDC algorithms, achieving a better storage gain ratio and enhancing both chunking and hashing throughput. Practically, our experiments show that BFBC is 10 times faster than basic sliding window (BSW) and approximately three times faster than two thresholds two divisors (TTTD). The proposed triple hash function algorithm is five times faster than SHA1 and MD5 and achieves a better deduplication elimination ratio (DER) than other CDC algorithms. The symmetry of our work is based on the balance between the proposed system performance parameters and its reflection on the system efficiency compared to other deduplication systems.