Fingerprint Processing Research Articles

Diskursus Anak Penderita Gangguan Jiwa Sebagai Korban Tindak Pidana Kekerasan Seksual Dalam Periskop Penegakan Hukum

Cases of sexual violence against children, especially those with mental disorders, are a serious problem that requires attention in law enforcement and child protection in Indonesia. In this context, the lack of regulations that specifically regulate the protection of children with mental disorders creates obstacles in handling cases and protecting victims. The problem raised in this research is: how is the law enforced for criminal acts of sexual violence committed against children with mental disorders and what efforts are made by investigators in this case to fulfill the rights of victims. This research is directed towards empirical legal research and the approach used in this research uses a statutory approach and a case approach. This research was carried out at Gorontalo Police. The results of the research and discussion explain that the law enforcement process for criminal acts of sexual violence against children who also experience mental disorders involves a series of steps that take into account the special conditions of the victims. Namely, the fulfillment of the victim's rights as a child with mental disorders, including: during the investigation and inquiry process the victim continues to be accompanied by P2TP2A, Social Services, psychologists and psychologists. This proves that the child is being looked after so that there is no trauma after the incident. In Indonesia there are no regulations that explicitly regulate this matter so investigators still use Article 81 paragraph 1 in conjunction with Article 76 e or Article 82 paragraph 1 in conjunction with Article 76 e of Republic of Indonesia Law No. 17 of 2016 concerning the Determination of Government Regulations in Lieu of Republic of Indonesia Law No. 1 of 2016 Regarding the 2nd Amendment to Republic of Indonesia Law No. 23 of 2002 concerning Child Protection into Law in conjunction with Article 64 paragraph 1 to 1 of the Criminal Code. With the general stages of the law enforcement process, namely, Reporting, Investigation, Examination of Victims, Collection of Evidence, Determination of Suspects, fingerprinting process, and then it is handed over to the prosecutor's office for the court process.

DNA recovery after sequential processing of latent fingerprints on black polyethylene plastic.

Latent fingerprints on plastic substrates can be visualized by using sequential treatments to enhance the contrast between the fingerprint residues and underlying substrate; however, the extent to which these processes affect subsequent DNA analysis is mostly unknown. Latent fingerprints deposited on black plastic by one donor were visualized with single-process fingerprint powders (i.e., white powder, bichromatic powder, or bichromatic magnetic powder) or sequential treatments (i.e., laser → reflected ultraviolet imaging system (RUVIS) → CA fuming → RUVIS → Rhodamine 6G, Ardrox, and MBD (RAM) or CA fuming → RAM/laser → bichromatic magnetic powder). Samples were examined after the addition of each treatment. DNA was collected using cotton swabs, extracted, quantified, and amplified. DNA yields, peak heights, number of alleles obtained, and percentage of DNA profiles eligible for CODIS upload were examined. Latent fingerprints processed with the laser and up to three sequential treatments generated DNA profiles with significantly higher peaks heights than those of the untreated samples. Fingerprints processed with the laser and up to two sequential treatments generated DNA profiles with significantly more alleles. All methods beginning with laser enhancement generated more CODIS-eligible profiles. Additional research is needed to determine the extent to which initial laser enhancement impacts the success of downstream DNA profiling results. Although DNA profile development is not guaranteed due to the variable quantities of DNA contained within latent fingerprints, the selection of an appropriate latent fingerprint visualization method could maximize both fingerprint detection and the generation of CODIS-eligible DNA profiles.

Fingerprint Recognition System

The abstract provides a brief study of the widely used technology that is fingerprint, discussing issues and challenges in the field. It highlights fingerprint databases and evaluation campaigns, specifically focusing on the Bio Secure Benchmarking Framework for Fingerprints. The framework utilizes the NIST Fingerprint Image Software (NFIS2) and the MCYT-100 database, employing two evaluation protocols. The abstract compares two research systems within the proposed framework, detailing their different approaches to fingerprint processing. Fusion experiments involving various combinations of these systems are discussed, and the NFIS2 software is used to obtain fingerprint scores for multimodal experiments conducted in the Bio Secure Multimodal Evaluation Campaign (BMEC’2007). Keywords—Computer graphics, Artificial intelligence, Image recognition, Fingerprint recognition.

Quantifying DNA loss in laboratory-created latent prints due to fingerprint processing

Fingerprints, which are associated with touch samples, typically contain a limited amount of DNA. The amount of available DNA can be further reduced when the same touch samples undergo fingerprint processing [1]. The fingerprint development process consists of high-powered lighting (inherent luminescence and UV light) and chemical compounds (ninhydrin, black powder, cyanoacrylate, and rhodamine 6 G) which could reduce DNA quality and quantity. Therefore, forensic scientists often must select one type of analysis over the other due to the destructive nature of processing. DNA and latent fingerprinting are both useful sources for identification, although both can produce partial results. A partial DNA profile may only contain a few alleles, limiting the ability to identify a potential suspect to perform comparisons. A partial fingerprint generally means that only a very small part of the fingerprint is present, which makes comparisons difficult. Because partial results are common, combining data from both fingerprinting and DNA analysis would increase the confidence of an identification of a person. Significant research has been performed to determine if a DNA profile can be obtained from latent processed fingerprints; however, there has yet to be research done in a standardized manner. In this study, we used standardized mock “fingerprints” in order to reduce fingerprint DNA variability and specifically focused on DNA quantitation after each step in the fingerprinting process. Results suggest that latent print processing techniques used on non-porous surfaces (plastic, duct-tape, metal, and rubber) do not affect DNA quantity or quality. In contrast, ninhydrin, a chemical used for processing fingerprints present on porous surfaces (wood and paper), significantly reduced DNA recovery. Together these results suggest that DNA can still be performed on latent print processed items, unless ninhydrin has been used.

Laser Range Scanners for Enabling Zero-overhead WiFi-based Indoor Localization System

Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Toward achieving this goal, WiFi fingerprinting-based indoor localization systems have been proposed. However, fingerprinting involves significant effort—especially when done at high density—and needs to be repeated with any change in the deployment area. While a number of recent systems have been introduced to reduce the calibration effort, these still trade overhead with accuracy. This article presents LiPhi++ , an accurate system for enabling fingerprinting-based indoor localization systems without the associated data collection overhead. This is achieved by leveraging the sensing capability of transportable laser range scanners to automatically label WiFi scans, which can subsequently be used to build (and maintain) a fingerprint database. As part of its design, LiPhi++ leverages this database to train a deep long short-term memory network utilizing the signal strength history from the detected access points. LiPhi++ also has provisions for handling practical deployment issues, including the noisy wireless environment, heterogeneous devices, among others. Evaluation of LiPhi++ using Android phones in two realistic testbeds shows that it can match the performance of manual fingerprinting techniques under the same deployment conditions without the overhead associated with the traditional fingerprinting process. In addition, LiPhi++ improves upon the median localization accuracy obtained from crowdsourcing-based and fingerprinting-based systems by 284% and 418%, respectively, when tested with data collected a few months later.

Fingerprint Recognition Using Deep Learning - A Review

There have been efforts to address the problems with fingerprint identification systems that require physical contact by creating contactless fingerprint identification systems. Numerous studies on various aspects of contactless fingerprint processing, including the use of deep learning in various algorithmic frameworks, classical image processing, and the machine-learning pipeline, have been published. It was demonstrated that the deep learning-based solutions were more accurate than the alternatives. This effort was driven by a desire to provide a thorough assessment of these successes and their identified limitations. This study examined three approaches to contactless fingerprint recognition: (i) methods for capturing images of the fingerprint, (ii) traditional preprocessing techniques for enhancing fingerprint images for recognition tasks, and (iii) deep learning. (i) taking a picture of your finger, and (ii) using conventional image processing to get the picture ready for recognition. In total, eight research papers were found to meet both the inclusion and exclusion criteria. Based on this review's findings, we discussed the potential benefits of deep learning methods for biometrics and the challenges that still need to be overcome before these methods can be used in practical biometric settings.

Wi-Fi Fingerprinting for Indoor Positioning

Wireless Fidelity (Wi-Fi) fingerprinting is a remarkable approach developed by modern science to detect the user’s location efficiently. Today, the Global Positioning System (GPS) is used to keep track of our current location for outdoor positioning. In GPS technology, satellite signals cannot reach indoor environments as they are shielded from obstructions so that indoor environments with a lack of Line of Sight (LoS) do not provide enough satellite signal accuracy. Since indoor environments are very difficult to track, thus, a wide variety of techniques for dealing with them have been suggested. The best way to offer an indoor positioning service with the current technology is Wi-Fi since most commercial infrastructure is well equipped with Wi-Fi routers. The use of Wi-Fi fingerprinting techniques is becoming more common in the field of indoor positioning systems (IPS). For the purpose of indoor location localization, each method for obtaining a Wi-Fi fingerprint has been analyzed and discussed in this work. In this study, the majority of the algorithms that are associated with Wi-Fi fingerprinting have been interpreted, and the earlier works of other researchershave been critically evaluated, in order to get a more in-depth understanding of how the Wi-Fi fingerprinting process works.

Case Study on the Fingerprint Processing in a Workplace under GDPR Article 9 (2, b)

The protection of personal data is the most important legal standard for the use of biometric data. Fingerprints are personal biometric data in accordance with Article 9 (1) of the GDPR. It is also a category of personal data that needs to be processed specifically in order to ensure the right to the protection of personal data and to reduce the risk of its restriction. The problem discussed in this study is fingerprint processing in the workplace.

Contactless Fingerprint Recognition Using Deep Learning—A Systematic Review

Contactless fingerprint identification systems have been introduced to address the deficiencies of contact-based fingerprint systems. A number of studies have been reported regarding contactless fingerprint processing, including classical image processing, the machine-learning pipeline, and a number of deep-learning-based algorithms. The deep-learning-based methods were reported to have higher accuracies than their counterparts. This study was thus motivated to present a systematic review of these successes and the reported limitations. Three methods were researched for this review: (i) the finger photo capture method and corresponding image sensors, (ii) the classical preprocessing method to prepare a finger image for a recognition task, and (iii) the deep-learning approach for contactless fingerprint recognition. Eight scientific articles were identified that matched all inclusion and exclusion criteria. Based on inferences from this review, we have discussed how deep learning methods could benefit the field of biometrics and the potential gaps that deep-learning approaches need to address for real-world biometric applications.

Automated ‘lights-out’ searching of all recovered fingerprints: Review of the current workflow for latent fingerprint processing in Queensland, Australia

The process of linking an offender to a crime scene via their fingerprints has historically required significant human effort to compare latent fingerprints recovered from the scene with known fingerprints of a suspect. Increasing the speed of such comparisons, whilst maintaining accuracy and reliability and minimising error, is crucial for providing rapid intelligence to police investigators. One major opportunity for streamlining fingerprint examination is the adaptation of ‘lights-out’ technology to the comparison and matching of latent fingerprints. Here, we review the development, trial and validation process undertaken by the Queensland Police Service (QPS), Australia, to support implementation of a lights-out latent (LOL) workflow for automated latent fingerprint searching that is fully integrated with the existing case management systems. Targeted trials were undertaken using random selections of previously identified latent fingerprints that were searched using the LOL workflow against a local 10-print database. The results suggested that the LOL workflow could identify up to 44% of latent fingerprints with minimal human intervention and supported its implementation for all latent fingerprint comparisons in QPS casework. Review of LOL casework comparison outcomes for 2019 revealed that LOL-based identifications contributed approximately one quarter of all fingerprint identifications. Several procedural and technical factors that influenced the speed and efficiency of the LOL workflow are discussed, along with opportunities for improvement and future validation as an expert system.

Robust satellite antenna fingerprinting under degradation using recurrent neural network

Antenna fingerprinting is critical for a range of physical-layer wireless security protocols to prevent eavesdropping. The fingerprinting process exploits manufacturing defects in the antenna that cause small imperfections in signal waveform, which are unique to each antenna and hence device identity. It is an established process for physical-layer wireless authentication with proven usage systems in terrestrial systems. The premise relies on accurate signal feature discovery from a large set of similar antennas and stable fingerprint patterns over the operational life of the antenna. However, in space, many low-cost satellite antennas suffer degradation from atomic oxygen (AO). This is particularly a problem for nano-satellites or impromptu temporary space antennas to establish an emergency link, both of which are designed to operate for a short time span and are currently not always afforded protective coating. Current antenna fingerprinting techniques only use Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) to take a snap-shot fingerprint before degradation, and hence fail to capture temporal variations due to degradation. Here, we show how we can perform robust antenna fingerprinting (99.34% accuracy) for up to 198 days under intense AO degradation damage using Recurrent Neural Networks (RNNs). We compare our RNN results with CNNs and SVM techniques using different signal features and for different Low-Earth Orbit (LEO) satellite scenarios. We believe this initial research can be further improved and has real-world impact on physical-layer security of short-term nano-satellite antennas in space.

A Survey of Binary Code Fingerprinting Approaches: Taxonomy, Methodologies, and Features

Binary code fingerprinting is crucial in many security applications. Examples include malware detection, software infringement, vulnerability analysis, and digital forensics. It is also useful for security researchers and reverse engineers since it enables high fidelity reasoning about the binary code such as revealing the functionality, authorship, libraries used, and vulnerabilities. Numerous studies have investigated binary code with the goal of extracting fingerprints that can illuminate the semantics of a target application. However, extracting fingerprints is a challenging task since a substantial amount of significant information will be lost during compilation, notably, variable and function naming, the original data and control flow structures, comments, semantic information, and the code layout. This article provides the first systematic review of existing binary code fingerprinting approaches and the contexts in which they are used. In addition, it discusses the applications that rely on binary code fingerprints, the information that can be captured during the fingerprinting process, and the approaches used and their implementations. It also addresses limitations and open questions related to the fingerprinting process and proposes future directions.

DNA recovery after sequential processing of latent fingerprints on copy paper.

Forensic examiners must determine whether both latent fingerprint development and DNA profiling can be performed on the same area of an evidence item and, if only one is possible, which examination offers the best chance for identification. Latent fingerprints can be enhanced by targeting different components of fingerprint residues with sequential chemical treatments. This study investigated the effects of single‐reagent and sequential latent fingerprint development processes on downstream DNA analysis to determine the point at which latent fingerprint development should be stopped to allow for DNA recovery. Latent fingerprints deposited on copy paper by one donor were developed using three sequential processes: 1,8‐diazafluoren‐9‐one (DFO) → ninhydrin → physical developer (PD); 1,2‐indanedione‐zinc (IND‐Zn) → ninhydrin → PD; and IND‐Zn → ninhydrin → Oil Red O (ORO) → PD. Samples were examined after the addition of each chemical treatment. DNA was collected with cotton swabs, extracted, quantified, and amplified. DNA yields, peak heights, number of alleles obtained, and percentage of DNA profiles eligible for CODIS upload were examined. DNA profiles were obtained with varying degrees of success, depending on the number and type of treatments used for latent fingerprint development. The treatments that were found to be the least harmful to downstream DNA analysis were IND‐Zn and IND‐Zn/laser, and the most detrimental treatments were DFO, DFO/laser, and PD. In general, as the number of treatments increase, the opportunities for DNA loss or damage also increase, and it is preferable to use fewer treatments when developing latent fingerprints prior to downstream DNA processing.

Materials Fingerprinting Classification

Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates. Methods for visualizing the local atomic structure, such as atom probe tomography (APT), which routinely generate datasets comprised of millions of atoms, are an important step in realizing this goal. However, state-of-the-art APT instruments generate noisy and sparse datasets that provide information about elemental type, but obscure atomic structures, thus limiting their subsequent value for materials discovery. The application of a materials fingerprinting process, a machine learning algorithm coupled with topological data analysis, provides an avenue by which here-to-fore unprecedented structural information can be extracted from an APT dataset. As a proof of concept, the material fingerprint is applied to high-entropy alloy APT datasets containing body-centered cubic (BCC) and face-centered cubic (FCC) crystal structures. A local atomic configuration centered on an arbitrary atom is assigned a topological descriptor, with which it can be characterized as a BCC or FCC lattice with near perfect accuracy, despite the inherent noise in the dataset. This successful identification of a fingerprint is a crucial first step in the development of algorithms which can extract more nuanced information, such as chemical ordering, from existing datasets of complex materials. Program summaryProgram Title: Materials FingerprintingCPC Library link to program files:https://doi.org/10.17632/2fhch3x85m.1Developer's repository link:https://github.com/maroulaslab/Materials-FingerprintingLicensing provisions: GPLv3Programming language: PythonSupplementary material: A user manual and examples are provided with the source code in the GitHub repository.Nature of problem: Atom probe tomography provides sub-nanometer resolution of a material, but due to noise and sparsity introduced by the process, the crystal structure of a material cannot presently be determined from the resulting data.Solution method: Our Materials Fingerprinting library presents a topologically informed machine learning methodology to classify the lattice structure of a material from atomic probe tomography data. We create persistence diagrams from small neighborhoods centered at each atom in the resulting APT data and use the summary statistics of a novel metric on the space of persistence diagrams as features for a classification algorithm.

An intelligent clustering method for devising the geochemical fingerprint of underground aquifers

Geochemical fingerprinting is a rapidly expanding discipline in the earth and environmental sciences, anchored in the recognition that geological processes leave behind physical, chemical and sometimes also isotopic patterns in the samples. Furthermore, the geochemical fingerprinting of natural cycles (water, carbon, soil and biota fingerprinting) are influenced by the anthropogenic impact and by the climate change. So, their monitoring is a tool of resilience and adaptation. In recent years, computational statistics and artificial intelligence methods have started to be used to help the process of geochemical fingerprinting. In this paper we consider data from 57 wells located in the province of Ferrara (Italy), all belonging to the same geological group and separated into 4 different aquifers. The aquifer from which each well extracts its water is known only in 18 of the 57 cases, while in other 39 cases it can be only hypothesized based on geological considerations. We devise a novel technique for geochemical fingerprinting of groundwater by means of which we are able to identify the exact aquifer from which a sample is extracted with a sufficiently high accuracy. Then, we experimentally prove that out method is sensibly more accurate than typical statistical approaches, such as principal component analysis, for this particular problem.

Smartphones for latent fingerprint processing and photography: A revolution in forensic science

AbstractSmartphones are a great technological revolution of the last decade. Their characteristics, such as high‐resolution cameras, internal storage, and processing capacity, make them useful for different technical solutions, such as forensics. The latent fingerprint is a critical piece of evidence in crime scenes; it is invisible to the naked eye, so different methods for improving visualization are essential. Several techniques, based on chemical and physical processes, have been developed over time. Smartphones can help improve visualization through optical features. Recent studies have evaluated combining methods of acquiring latent fingerprints with smartphones, showing different principles for this purpose. Using this type of tool remains controversial, especially regarding its admissibility in court. Studies in this area are necessary since these tools would make the lives of forensic scientists much easier. Among the main reasons for its application in crime scenes are ease of use, wide availability for purchase, and the low cost involved, which is of great interest to developing countries. This work aims to show that a smartphone camera is also a suitable tool for enhancing and acquiring latent digital fingerprints with sufficient quality for forensic use. This review shows further research and discusses image quality, admissibility in court, and its importance for developing countries due to its low cost.This article is categorized under: Digital and Multimedia Science > Mobile Forensics Crime Scene Investigation > From Traces to Intelligence and Evidence Digital and Multimedia Science > Multimedia Forensics

Características dermatoglíficas e nível de flexibilidade de escolares de 8 a 17 anos do município de Joaçaba/SC

O objetivo do estudo foi verificar as características dermatoglíficas e o nível de flexibilidade de escolares de 8 a 17 anos do município de Joaçaba/SC, comparando o perfil de meninos e meninas. Foram investigados 1.869 escolares, sendo 922 meninos e 947 meninas. Para coleta de dados, foram utilizados o leitor dermatoglífico, para registro, processamento e análise das impressões digitais e o de teste de sentar e alcançar com banco de Wells e Dillon para avaliação da flexibilidade. Os resultados mostraram que os valores médios de verticilo (W), somatório da quantidade total de linhas (SQTL) e Presilha radial (LR), foram superiores para sexo masculino, enquanto que os valores de presilha ulnar (LU), de arco (A) e de flexibilidade foram superiores para o sexo feminino. Pode-se concluir, em relação a dermatoglifia, que o sexo masculino teria maior potencial para a resistência aeróbica e coordenação motora, enquanto que o sexo feminino teria maior potencial para velocidade, força e flexibilidade.

Survey of CSI fingerprinting‐based indoor positioning and mobility tracking systems

Techniques for indoor positioning systems (IPSs) can be categorised as range-based or range-free. Range-based methods rely on geometric mappings to approximate a location given the calculated distances or angles from multiple reference points. In contrast, range-free strategies utilise fingerprinting, wherein an acquired fingerprint data is compared to a pre-collected dataset to identify the best position estimate. Among these, fingerprinting of channel state information (CSI) is preferred over other information such as received signal strength indicator as the former can exploit the effect of multipath propagation and is robust against non-line-of-sight channels. CSI has the potential to achieve cm-level positioning accuracy with a single reference point only. In this study, the authors survey CSI fingerprinting-based indoor positioning and mobility tracking systems. The process of fingerprinting that includes site surveying and signal preprocessing is discussed in detail. They determine the potential challenges of such systems and propose remedies to improve positioning accuracy. In general, spatial diversity, such as multiple-input multiple-output antennas and wireless sensor networks, or frequency diversity (e.g. high subcarrier count, frequency hopping mechanism) are exploited to achieve high positioning resolution. Such IPS can also be enhanced via additional sensors or spatial graphs for motion detection and tracking.

GEFF: Graph embedding for functional fingerprinting

It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared. Here we propose an embedding framework, GEFF (Graph Embedding for Functional Fingerprinting), based on group-level decomposition of FCs into eigenvectors. GEFF creates an eigenspace representation of a group of subjects using one or more task FCs (Learning Stage). In the Identification Stage, we compare new instances of FCs from the Learning subjects within this eigenspace (validation dataset). The validation dataset contains FCs either from the same tasks as the Learning dataset or from the remaining tasks that were not included in Learning. Assessment of validation FCs within the eigenspace results in significantly increased subject-identification rates for all fMRI tasks tested and potentially task-independent fingerprinting process. It is noteworthy that combining resting-state with one fMRI task for GEFF Learning Stage covers most of the cognitive space for subject identification. Thus, while designing an experiment, one could choose a task fMRI to ask a specific question and combine it with resting-state fMRI to extract maximum subject differentiability using GEFF. In addition to subject-identification, GEFF was also used for identification of cognitive states, i.e. to identify the task associated to a given FC, regardless of the subject being already in the Learning dataset or not (subject-independent task-identification). In addition, we also show that eigenvectors from the Learning Stage can be characterized as task- and subject-dominant, subject-dominant or neither, using two-way ANOVA of their corresponding loadings, providing a deeper insight into the extent of variance in functional connectivity across individuals and cognitive states.

Relative Angle Correction for Distance Estimation Using K-Nearest Neighbors

Indoor position estimation has been cited as not being accurate due to noisy environments. Variations of the k-nearest neighbor (kNN) algorithm have been used to mitigate this noise. In this paper, the concept of using the fingerprinting process with the radiation pattern of an antenna is explored. The contents of the paper focus on finding the distance between an RFID reader and a tag. Using the relative angle of the antenna with respect to a reference point, a model is introduced. Received signal strength index (RSSI) readings from the backscattered signal of the RFID tag were measured in a 7-foot $\times \,\,4$ -foot grid. The resulting data points were placed in the relative angle correction KNN (RAC-KNN) algorithm. Both regression and classification models of the RAC-KNN were implemented. As a result, the classification model yielded an 85% prediction rate of the distance estimation and the regression model resulted in a 0.2-foot average error.