Automatic Fingerprint Identification Research Articles

The effects and benefits of a latent print AFIS deferral policy

Forensic laboratories in the United States had an estimated backlog of 570,100 requests for all forensic services at the end of 2014. Latent print requests represented approximately 12% of that total backlog [1, NIJ 2019]. With ever-increasing demands and backlogs, a review of laboratory or section practices becomes vital to operations. Work process and business practice changes can increase efficiencies and result in the reduction of casework backlogs and turnaround times. The automated fingerprint identification system (AFIS) deferral policy implemented by the Latent Print Comparative Section (LPCS) of the Phoenix Police Department Laboratory Services Bureau (PPD LSB) was employed to address the latent print backlog. Five years of multiple AFIS request types were analyzed to demonstrate the positive effects and benefits of such policies, including a 26.32% decrease in turnaround time over the data collection and analysis period and a 90.96% reduction in backlogged requests for one year.

Humans and machine: computer-assisted consensus amongst forensic examiners

ABSTRACT Technology has played a critical role in enhancing the effectiveness and development of forensic processes. Digitalization and the use of automated biometric technologies, like the Automated Fingerprint Identification Systems (AFIS) for instance, has been a significant step forward, increasing accountability and transparency, and improving the accuracy and efficiency of evidence processing. Traditionally these systems are search systems only, and require the implementation of additional workflow strategies to support forensic processes in best practice approaches. More recently, the introduction of statistical models represents strategies to strengthen the reliability of forensic science by combining human-based methods with computer-assisted approaches.

Issues and Challenges Encountered by Police Officers in Automated Fingerprint Identification System Operation

Automated Fingerprint Identification Systems (AFIS) play a vital role in modern law enforcement by aiding in the identification and apprehension of criminals through fingerprint analysis. However, despite their significance, challenges in operating AFIS remain the main hindrance to its effective utilization. Thus, this study aims to investigate the issues and challenges faced by police officers in the Philippines with AFIS. Employing a convergent parallel mixed methods research design, quantitative data were collected through a survey questionnaire, while qualitative data were obtained through in-depth interviews. The study participants comprised six experienced Filipino police officers purposively chosen for their exposure to AFIS operations. The study uses the mixed method. Descriptive statistics and Mann-Whitney U Test were used during quantitative data analysis, while thematic analysis was used in analyzing qualitative data. The study findings revealed that the top challenge faced by police officers in the Philippines is comparing and storing fingerprints in the AFIS database, primarily due to insufficient training and technical support. Themes such as regular training, technical support, and continuous learning emerged to address the challenges related to fingerprint quality, technical issues, storage limitations, and hardware problems. The study also revealed that Filipino police officers encounter moderate challenges when operating AFIS, with variations depending on sex, age, and length of service. In conclusion, the study underscores the significance of continuous training, technical support, and effective communication for successful AFIS operations. Law enforcement agencies are urged to prioritize providing proper training and technical support to ensure accurate and reliable utilization of this technology in policing.

Development, detection and decipherment of obfuscated fingerprints in humans: Implications for forensic casework.

Fingerprints have been widely used and accepted as an effective method of human identification. This biometric tool aids in criminal investigations for personal identity for over a century. Whilst the Automatic Fingerprint Identification System (AFIS) has bolstered security efforts, it has also opened doors to potential scams, affecting both civilian and law enforcement operations. Despite extensive research on fingerprint authentication issues, very little attention has been given to addressing the problem of fingerprint alteration or obfuscation. Fraudsters, with the guidance of experts, have developed new techniques to obscure their fingerprints intentionally. Fingerprint obfuscation is the deliberate alteration of fingerprint patterns with the aim of concealing their true identity, raising concerns amongst security and investigative organizations. The objective of the current communication is to highlight the numerous techniques used for obfuscation, forgery and alteration of fingerprints in humans. It further accentuates the need for identification and interpretation of these altered fingerprints and recommends notifying law enforcement agencies of potential threats.

Socio-Legal Review of Electronic Data Protection in Criminal Policy Formulation in the Era of Globalization

The threat of misuse of personal data in Indonesia is increasing, in particular since the government introduced the electronic identity card (e-KTP) program and plans for the police to use it to build the Indonesian Automatic Fingerprint Identification System (INAFIS). However, the plan was eventually canceled by the police because seen overlapping with the e-KTP program. Then the government records personal data, and the private sector such as banks and telecommunications service providers also records the data. The purpose of this paper is to analyze electronic data protection which has become a Cyber Cream in the current era of globalization as well as What is the review of the criminal policy formulation in the electronic data protection effort. With method writing normative juridical. In conclusion, writing This is b a form of legal protection that should be given to victims of criminal acts that have not yet been regulated by Law No. 11 of 2008 related to Information and Electronic Transactions in Indonesia. One of the most common crime threats today is the theft of personal information, in which other people's important data is stolen. As well as the approach to preventing and handling crime by using penal means is a "penal policy" or "penal law enforcement policy" which is carried out through several stages, namely: formulation of legislative policies, application of judicial policies, and execution of executive or administrative policies.

Fluorescent‐Dye‐Doped Nanoporous Silica Powder for Imaging of Latent Fingerprints (Levels 1–3) and Case Studies

AbstractNovel naphthalimide based fluorescent dye NI‐1 possess (i) high quantum yield, (ii) blue emission (λem=450 nm), (iii) CIE coordinate (x=0.21, y=0.35) with 39 % blue colour purity in solid state, (iv) photochemical and thermal stability up to 200 oC and (v) low cytotoxicity to normal cells L929. NI‐1 was doped in nano porous silica and used as fluorescent powder for imaging of latent fingerprints (LFPs) on coloured surfaces, barcode, wood, brick, leaf and engraved steel beside other common household porous and nonporous surfaces. We demonstrated visibility of levels 1–3 on ceramic tile, glass, paper, steel, iron and compact disc using NI‐1 doped fluorescent silica powder. Furthermore, LFPs developed with NI‐1 were successfully lifted from different surfaces using adhesive tape for documentation and archiving purposes. To mimic the automated fingerprint identification system (AFIS), we manually screened fingerprints from 16 subjects to identify the unknown subject. The unique minutiae points such as long hook, opposite bifurcation, crossbar, cross over, dock, triple bifurcation, lake and spikes were observed beside other 16 minutiae points.

Two No-Reference Image Quality Assessment methods based on Possibilistic Choquet Integral and Entropy: Application to Automatic Fingerprint Identification Systems

Two No-Reference Image Quality Assessment methods based on Possibilistic Choquet Integral and Entropy: Application to Automatic Fingerprint Identification Systems

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

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

Multi-Layered Minutiae Extraction Based on Fusion-Attention for OCT Fingerprints

Minutiae extraction plays an important role in automated fingerprint identification systems. There are many minutiae extraction methods proposed for various fingerprints (rolled/slap/latent) in the past decades. However, for fingerprints by optical coherence tomography (OCT) imaging which have the essential property of multi-layered tissue structure, previous work may not be suitable. This paper is devoted to extract minutiae for OCT-based fingerprints based on the characteristics of OCT imaging and powerful deep neural networks. Specifically, we propose a multi-tissue-layered minutiae extraction network based on fusion-attention, involving a multi-tissue-layered feature extractor to fully explore the rich information in multiple subsurface fingerprints, and a one-stage detection framework to detect minutiae points. To demonstrate the effectiveness and robustness of the proposed method, a series of experiments have been carried out on the public OCT fingerprint benchmark dataset (https://github.com/FengLiu-0013/). Our method obtains the best results with an improvement of 8.3% in F1-score compared with state-of-the-art methods. The proposed method has also been evaluated for fingerprint identification using the extracted minutiae. Our method has achieved a matching error rate of 1.57%, with a relative reduction of 41% compared with state-of-the-art methods.

An Innovative Possibilistic Fingerprint Quality Assessment (PFQA) Filter to Improve the Recognition Rate of a Level-2 AFIS.

In this paper, we propose an innovative approach to improve the performance of an Automatic Fingerprint Identification System (AFIS). The method is based on the design of a Possibilistic Fingerprint Quality Assessment (PFQA) filter where ground truths of fingerprint images of effective and ineffective quality are built by learning. The first approach, QS_I, is based on the AFIS decision for the image without considering its paired image to decide its effectiveness or ineffectiveness. The second approach, QS_PI, is based on the AFIS decision when considering the pair (effective image, ineffective image). The two ground truths (effective/ineffective) are used to design the PFQA filter. PFQA discards the images for which the AFIS does not generate a correct decision. The proposed intervention does not affect how the AFIS works but ensures a selection of the input images, recognizing the most suitable ones to reach the AFIS's highest recognition rate (RR). The performance of PFQA is evaluated on two experimental databases using two conventional AFIS, and a comparison is made with four current fingerprint image quality assessment (IQA) methods. The results show that an AFIS using PFQA can improve its RR by roughly 10% over an AFIS not using an IQA method. However, compared to other fingerprint IQA methods using the same AFIS, the RR improvement is more modest, in a 5-6% range.

Research on the local regional similarity of automatic fingerprint identification system fingerprints based on close non‐matches in a ten million people database—Taking the central region of whorl as an example

The local regional similarity of fingerprints has always been a hot issue in the field of fingerprint research. With the increasing size of ten-print databases, the appearance of close non-matches (CNMs) in automatic fingerprint identification system (AFIS) candidate lists has attracted increasing attention from forensic science departments worldwide. In this study, three categories (high-, medium-, and low-level) of standards for CNMs were established and 60 whorl samples were marked with different numbers of minutiae to explore the occurrence and influencing factors of CNMs in AFIS candidate lists based on a ten million people database. The results showed that all prints could be found with their corresponding CNMs. The average occurrence rate of CNMs for every query was 52.7% in the top 100 lists, and the most similar CNM was exactly the same in the local area of 12 coincidence points. CNMs appeared more in the middle and lower parts of the central region of the whorl. Moreover, shorter C2C distances and the same finger number and hand led to more CNMs being inspected. CNMs with higher similarity required a more extensive regional area and smaller minutiae density. We concluded that CNMs have a high occurrence rate in large-scale databases and many factors are closely related to them. Fingerprint examiners and researchers need to strengthen their understanding of CNMs to avoid the occurrence of misidentification like the Madrid bombings.

Toward better AFIS practice and process in the forensic fingerprint environment

Automated Fingerprint Identification Systems (AFIS) are used in forensic operational environments worldwide. Traditionally, these systems are search systems only and have limited workflow to support forensic processes in recommended best-practice approaches. This paper addresses this issue by presenting best practice approaches and processes for AFIS in forensic environments. The discussion is divided into three parts. First, we identify three factors that can impact the performance of AFIS in forensic environments. Second, we discuss the search process and highlight how the National Police of the Netherlands (NPN) strategies to mitigate bias and error in their AFIS workflow. Finally, we briefly discuss other considerations for establishing best-practice. We offer resources for biometric managers and users, providing an overview of the best AFIS practices in forensic environments. We propose the NPN system as a benchmark and aim to foster discussions among AFIS communities to enhance AFIS practices and processes in forensic fingerprint environments.

Multifunctional Eu(III)-modified HOFs: roxarsone and aristolochic acid carcinogen monitoring and latent fingerprint identification based on artificial intelligence.

The exploration of multifunctional materials and intelligent technologies used for fluorescence sensing and latent fingerprint (LFP) identification is a research hotspot of material science. In this study, an emerging crystalline luminescent material, Eu3+-functionalized hydrogen-bonded organic framework (Eu@HOF-BTB, Eu@1), is fabricated successfully. Eu@1 can emit purple red fluorescence with a high photoluminescence quantum yield of 36.82%. Combined with artificial intelligence (AI) algorithms including support vector machine, principal component analysis, and hierarchical clustering analysis, Eu@1 as a sensor can concurrently distinguish two carcinogens, roxarsone and aristolochic acid, based on different mechanisms. The sensing process exhibits high selectivity, high efficiency, and excellent anti-interference. Meanwhile, Eu@1 is also an excellent eikonogen for LFP identification with high-resolution and high-contrast. Based on an automatic fingerprint identification system, the simultaneous differentiation of two fingerprint images is achieved. Moreover, a simulation experiment of criminal arrest is conducted. By virtue of the Alexnet-based fingerprint analysis platform of AI, unknown LFPs can be compared with a database to identify the criminal within one second with over 90% recognition accuracy. With AI technology, HOFs are applied for the first time in the LFP identification field, which provides a new material and solution for investigators to track criminal clues and handle cases efficiently.

Automated Fingerprint Identification System: with and without the Possibility of Correction of a Digitalised Image

Automated Fingerprint Identification System: with and without the Possibility of Correction of a Digitalised Image

Information loss, contextual information, and distinctiveness influence how well novice analysts discriminate fingerprints

AbstractAcross two experimental studies, we investigated the role of information loss, contextual information, and distinctive features of fingerprints on novice's ability to judge whether two fingerprints came from the same source. Distinctive fingerprints resulted in more accurate decisions. Information loss diminished performance on the comparison task, likely due to the loss of distinctive features. When people received contextual information from a fingerprint matching software (Automated Fingerprint Identification System or AFIS) suggesting that the two fingerprints were from the same source, they were less accurate and more biased to render a “same source” decision. These findings suggest that analysts should evaluate the characteristics of a fingerprint prior to engaging in a comparison task, and that fingerprints lacking information, particularly distinctive characteristics, should be more cautiously evaluated.

A Scattering Wavelet Network-based Approach to Fingerprint Classification

In a large-scale automatic fingerprint identification system (AFIS), fingerprint classification is an essential indexing step to reduce the search time in a large database for accurate matching. Fingerprint classification is still a challenging machine learning problem due to large intra-class and small inter-class variability. Nonlinear elastic deformation is one of the main sources of intra-class variability, which occurs due to the non-uniform pressure applied during fingerprint acquisition and the elastic nature of the fingerprint itself. This paper proposes a novel approach to fingerprint classification based on a scattering wavelet network to extract translation and small deformation invariant local features. The resulting sparse invariant feature vectors are used as input to a simple generative PCA affine classifier for the classification. The experiments evaluated with two different protocols on a benchmark NIST SD-4 database show the effectiveness and robustness of the proposed fingerprint classification algorithm in terms of classification accuracy.

FLD-SRC: Fingerprint Liveness Detection for AFIS Based on Spatial Ridges Continuity

Automatic fingerprint identification system (AFIS) uses fingerprint to authenticate users, which is legal if the user is enrolled. However, numerous studies reveal that it is susceptible to spoofing attacks where a third person might freely synthesize counterfeit fingerprints to trick the scanner. To resist spoofing attacks, it makes fingerprint liveness detection (FLD) highly desirable. Most of previous work was to directly input the whole fingerprints into convolutional neural network, making it impossible to fully explore the relationship of spatial ridges, especially those with the latent fine-grained minutia on fingerprint ridges. Accordingly, in this paper, we exploit the relationship of spatial ridges in fingerprints and propose a novel FLD method based on spatial ridges continuity (FLD-SRC). Several fingerprint patches are first selected utilizing ridge texture saturation, and then uniformly split into several slices and thus construct the spatial continuity between pixels and between slices. Next, the proposed FLD-SRC learns deep features from fingerprints and eliminates redundant information. After that, the extracted feature maps are treated as a sequence and analyzed the intra-continuity by cascade gated recurrent unit (GRU). A discriminant slice grouping subnetwork is then developed to model the correlation between ridges slices and implicitly discover the discriminant inter-continuity. Pruning strategy is further utilized to reduce network parameters and promote its practical application in real scenarios. Experimental results, evaluated on three publicly available datasets, show the competitiveness of our method. Furthermore, in addition to reducing computational complexity, our method also shows the best ACE performance in cross-material and cross-sensor cases.

Pattern Mathematical Model for Fingerprint Security Using Bifurcation Minutiae Extraction and Neural Network Feature Selection

Biometric based access control is becoming increasingly popular in the current era because of its simplicity and user-friendliness. This eliminates identity recognition manual work and enables automated processing. The fingerprint is one of the most important biometrics that can be easily captured in an uncontrolled environment without human cooperation. It is important to reduce the time consumption during the comparison process in automated fingerprint identification systems when dealing with a large database. Fingerprint classification enables this objective to be accomplished by splitting fingerprints into several categories, but it still poses some difficulties because of the wide intraclass variations and the limited interclass variations since most fingerprint datasets are not categories. In this paper, we propose a classification and matching fingerprint model, and the classification classifies fingerprints into three main categories (arch, loop, and whorl) based on a pattern mathematical model using GoogleNet, AlexNet, and ResNet Convolutional Neural Network (CNN) architecture and matching techniques based on bifurcation minutiae extraction. The proposed model was implemented and tested using MATLAB based on the FVC2004 dataset. The obtained result shows that the accuracy for classification is 100%, 75%, and 43.75% for GoogleNet, ResNet, and AlexNet, respectively. The time required to build a model is 262, 55, and 28 seconds for GoogleNet, ResNet, and AlexNet, respectively.

Amorphous calcium phosphate nanoparticles allow fingerprint detection via self-activated luminescence

Herein, stable activator-free photoluminescent nanoparticles based on amorphous calcium phosphate (ACP) were obtained for the first time, and their performance for latent fingerprint imaging was investigated. ACP nanoparticles with irregular rounded shape and diameters in 10–40 nm range were prepared by a rapid and simple chemical precipitation followed by heat treatment at 400 °C for 4 h. Notably, diffuse reflectance spectroscopy, photoluminescence excitation and emission measurements reveal a high density of localized energy states within the wide optical band gap of heat-treated ACP (Eg = 5.25–5.42 eV). This behavior allowed the excitation of ACP in near-ultraviolet region (λexc = 450 nm, 2.75 eV), leading to an intense defect-related broadband (490–890 nm) photoluminescence emission centered at 540 nm (2.30 eV), 50 times more intense than untreated nanoparticles. The effect of lattice shrinkage due to structural water elimination, the presence of ionic vacancies (VCa and VO in PO43-) and carbonate groups (CO32-), on the luminescent properties of ACP were discussed in detail, as well as investigated after aqueous-mediated ACP crystallization into hydroxyapatite (HA). ACP nanoparticles were non-cytotoxic, as determined by MTT assay in Human Dermal Fibroblast neonatal (HDFn) cell line, with cell viabilities superior to 95% in all tested concentrations (20–320 μg/mL), after incubation for 24 and 48 h. Latent fingerprint images were obtained using the ACP nanoparticles under near-ultraviolet irradiation (λexc = 450 nm) in tweezers and LCD smartphone and successfully validated by the Integrated Automated Fingerprint Identification System used by Scientific Police in Spain. The present results evidenced that the new luminescent ACP nanoparticles are safe to be used and agree with the forensic requirements for future legal actions.

Fingerprint Liveness Detection Schemes: A Review on Presentation Attack

ABSTRACT Extant literature has highlighted the vulnerability of Automatic Fingerprint Identification System (AFIS) to various forms of attacks, indicating presentation attack as the most predominant. This form of attack involves the malicious utilization of ubiquitous materials such as silicone, gelatin, playdoh, among many others, to fabricate synthetic fingerprints to circumvent AFIS. As a result, various studies have posited some countermeasures, encompassing the use of hardware- based and software-based techniques. The hardware-based methods necessitate the integration of supplementary sensors for capturing other live human traits such as pulse rate, odour etc. Conversely, the software-based methods are focused on feature extraction and deep learning schemes. However, despite the robustness of the software-based approach as compared to the hardware-based technique, both schemes are still faced with an immense challenge in developing fingerprint spoof generalized models to mitigate the concern of cross-material (novel) detection. As a result, various studies have highlighted that the issue of fingerprint spoofing should be treated an” opened-set problem” (training only live fingerprints), rather than a” closed-set problem” (training live & spoof), birthing the development of fingerprint one-class classifiers. This article presents a comprehensive detail on fingerprint spoofing, extant countermeasures and the challenge still faced by fingerprint spoof detectors.