Fingerprint Identification System Research Articles

Non-invasive forensic identification of excavated human remains: capturing surface and internal fingerprints using optical coherence tomography.

Unidentified human remains are frequently found in missing person cases, necessitating identification for forensic purposes and to inform the next of kin. Traditional postmortem fingerprinting methods depend on intact surface fingerprints, which are often compromised by decomposition. A viable alternative is to use internal fingerprints(a blueprint of the surface fingerprint located just below the epidermis) instead. This study assessed the utility of Optical Coherence Tomography (OCT) as a means to record internal fingerprints from excavated human fingers. Conducted at the Amsterdam human taphonomic test site, the investigation comprised two longitudinal studies and two in situ burial scenarios. Human fingers were buried, excavated, and scanned using OCT at various time intervals.Internal fingerprints could be recorded up to 7 days longer than surface fingerprints, with a maximum of 10 days post-burial. These internal fingerprints provided higher minutiae counts, suitable for Automated Fingerprint Identification System (AFIS) searches. Additionally, in one case, fingerprints were successfully extracted after 13 weeks and 10 months of in situ burial. This demonstrates OCT's potential to enhance postmortem fingerprinting for identifying human remains in forensic investigations.

Development of Latent Fingerprints on Wet, Non-Porous Surfaces by Small Particle Reagent with Zinc Oxide Nanoparticles

Small particle reagent (SPR) technique is a method used for detecting latent fingerprints on wet, non-porous surfaces by suspending nanoparticles in a reagent that binds with fatty acid residues on fingerprints. The small particle reagent technique is particularly effective for detecting latent fingerprints on evidence that has been submerged underwater for prolonged periods. However, due to its reliance on imported reagents, SPR is often expensive and involves a lengthy waiting period. Consequently, many researchers have attempted to develop SPR formulations using easily obtainable, cost-effective, and safe chemicals. In this study, we prepared two new SPR formulations containing zinc oxide nanoparticles mixed with diethylene glycol monoethyl ether. Both formulations used a surfactant composed of two types (sodium tetradecyl sulfate and Tergitol NP-9) for the development of latent fingerprints on wet surfaces. Latent fingerprints were deposited on four different non-porous surfaces, including stainless-steel spoons, glass slides, aluminum foil, and plastic slides. These surfaces were immersed in tap water for 1, 3, 7, 14 and 30 days. The resulting latent fingerprints were analyzed using the Mini Automated Fingerprint Identification System and were shown to produce clear, sharp, and detailed fingerprints on the non-porous surfaces. The best results were found on the surfaces of stainless-steel spoons with both formulations, where minutiae were detectable at more than 40 points even after the surfaces had been immersed in water for up to 30 days. This study provides forensic scientists and crime scene investigators with a faster and safer method for developing latent fingerprints using non-hazardous materials, which could serve as an alternative to conventional formulations.

An alternative approach to the detection of latent fingermarks using [Eu2(BDC)3(H2O)2], a luminescent non-toxic MOF powder.

Fingermarks are important forensic evidence for identifying people. In this work, luminescent MOF [Eu2(BDC)3(H2O)2] (herein referred as EuBDC) was tested as a potential latent fingermark (LF) luminescent developer powder and its acute toxicity evaluated following OECD protocol 423. The results showed that the powder can develop groomed LF on materials such as leather, plastic, metal, glass, cardboard, and aluminum. LFs aged up to 30 days, left on glass slides were developed and classified as level-3. The images presented high quality, enabling correct donor identification as well as through an Automated Fingerprint Identification System (AFIS) search. EuBDC also showed useful results as secondary technique for fixed cyanoacrylate LFs, especially on a reflective, multicolored and non-flat surfaces. Additionally, the EuBDC was tested on ungroomed fingermarks, developed on a split depletion series of successive deposits and compared to a commercially available luminescent powder. Development also occurred on ungroomed aged fingermarks; as a secondary technique for cyanoacrylate fuming; and on transparent adhesive tape when used as a suspension for the latter. Considering that development powders are frequently handled by Papilloscopists and that this may pose a health risk, the acute toxicity and of EuBDC and histopathological analysis were evaluated. The tests showed no signs of toxicity. Therefore, the EuBDC was classified in category 5 in the Globally Harmonized System classification, the least toxic category, with an LD50 >5000 mg/Kg. The set of results shows that EuBDC powder has the potential use as a fingermark developer, as well as being suitable for applications for non-toxic material.

Developing a Health Support System to Promote Care for the Elderly.

In light of the demographic shift towards an aging population, there is an increasing prevalence of dementia among the elderly. The negative impact on mental health is preventing individuals from taking proper care of themselves. For individuals requiring hospital care, those receiving home care, or as a precaution for a specific individual, it is advantageous to utilize monitoring equipment to track their biological parameters on an ongoing basis. This equipment can minimize the risk of serious accidents or severe health hazards. The objective of the present research project is to design an armband with an accurate location tracking system. This is of particular importance for individuals with dementia and Alzheimer's disease, who frequently leave their homes and are unable to find their way back. The proposed armband also includes a fingerprint identification system that allows only authorized personnel to use it. Furthermore, in hospitals and healthcare facilities the biometric identification system can be used to trace periodic medical or nursing visits. This process improves the reliability and transparency of healthcare. The test results indicate that the armband functions in accordance with the desired design specifications, with performance evaluation of the main features including fall detection, where a hit rate of 100% was obtained, a fingerprint recognition test demonstrating accuracy from 88% to 100% on high-quality samples, and a GPS tracking test determining position with a difference of between 1.8 and 2.1 m. The proposed solution may be of benefit to healthcare professionals, supported housing providers, elderly people as target users, or their family members.

MAGNETIC FINGERPRINTING, AFIS AND BEYOND IN FORENSIC SCIENCE: A REVIEW

This review analyzes fingerprint identification technologies from different perspectives with particular attention being paid to both the traditional methods and cutting-edge inventions. The study integrates the findings from a large body of literature, covering both historical developments, technological innovations, experimental research, and the practical applications of forensic science. This includes inkless methods like magnetic resonance fingerprinting and automated fingerprint identification systems (AFIS) along with their modern way of depositing fingerprints using nanomaterials and macromolecules. In addition, this paper discusses the challenges and limitations of fingerprint identification, particularly the technical problems, the legal and ethical considerations and the need of human intervention in AFIS technology. The review, through comparative analysis and the discussion of the findings from various studies, gives insights into the value of fingerprint identification in criminal investigations and shows the emerging trends and future prospects in the field. It also offers insights for the researchers and forensic scientists a roadmap to combine the novel technologies with the existing practices in forensic science which might enhance the reliability as well as the accuracy of fingerprint identification.

A robust fingerprint identification approach using a fuzzy system and novel rotation method

Forensic science has developed significantly in the last few decades. Its key role is to provide crime investigators with processed data obtained from the crime scene to achieve more accurate results presented in court. Biometrics has proved its robustness against various critical crimes encountered by forensics experts. Fingerprints are the most important biometric used until now due to their uniqueness and production low cost. The automated fingerprint identification system (AFIS) came into existence in the early 1960s through the cooperation of the countries: USA, UK, France, and Japan. Ever since it started to develop gradually because of the challenges found at the crime scenes such as fingerprints distortions and partial cuts which in turn can severely affect the final calculations made by experts. The vagueness of the results was the main motivation to build a robust fingerprint identification system that introduces new and enhanced methods in its stages to help experts make more accurate decisions. The proposed fingerprint identification system uses Fourier domain analysis for image enhancement, then the system cuts the image around the core point after applying the rotation and core point detection methods. After that, it calculates the similarity based on the distance between fingerprint histograms extracted using the Local Binary Pattern (LBP). The system's last step is to translate the results into a sensible form where it utilizes fuzziness to provide more possibilities for the answer. The proposed identification system showed high efficiency on FVC 2002 and FVC 2000 databases. For instance, the results of applying our system on FVC 2002 provided a set of three ordered matching candidates such that 97.5 % of the results provided the correct candidate as the first order, and the rest of 2.5 % provided the correct candidate as the second order.

Automated Fingerprint Identification System

Abstract: AFIS is regarded as one of the greatest advancements in biometric systems as it enables fast and efficient identification of an individual by their distinct fingerprint designs. This system employs the use of digital imaging, finger-power techniques and other technologies in the storage and comparison of fingerprints. With AFIS, fingerprint images are all digitized making it easier to carry out crime investigations, border control and general identification of persons. The strength of AFIS systems is evidenced by the increased accuracy levels along with the capacity to accommodate numerous records and still allow for fast searches by fingerprinting matching to existing images for law enforcement on file. Some problems nevertheless persist which include the quality of fingerprints and environmental interferences, however machine learning and artificial intelligence improvements are still being incorporated into the systems to make them work even better. This paper investigates the system architecture and components, operational aspects, and prospects for development of the AFIS technology, which is essential in contemporary informational security

Optimizing Touchless Fingerprint Identification: A Machine Learning Approach to Modelling and Performance Evaluation

This paper explored the modelling and performance analysis of a smartphone-based fingerprint identification system using Convolutional Neural Networks (CNN). The research developed a theoretical framework to validate picture-based fingerprint identification as a feasible alternative to traditional touch-based methods. A modified Automated Fingerprint Identification System (AFIS) model served as the study's foundation. To enhance the model's capabilities, data from two databases, IIT India and SOCOFing, were utilized. The evaluation of the CNN architecture focused on mobile device fingerprint recognition. It emphasized key processes such as data pre-processing, model training, and the optimization of the CNN through a Siamese-CNN approach to boost accuracy and efficiency. Python scripts developed for this purpose were converted to Android code using TensorFlow for deployment on Android devices. Performance metrics, including identification accuracy, processing speed, and resource utilization, were analysed to determine the system's feasibility. The results demonstrated that CNN-based fingerprint identification systems hold significant promise for delivering robust and reliable biometric authentication on smartphones, highlighting both their practical applications and limitations. decrease medical as well as financial burden, hence improving the management of cirrhotic patients. These predictors, however, need further work to validate reliability.

The highly efficient and stable near-infrared phosphor Ca3MgSnGe3O12: Cr3+ for multifunctional application

The investigation of efficient and stable near-infrared (NIR) luminescent materials is crucial for rapidly developing near-infrared spectroscopic techniques. Herein, NIR phosphors Ca3MgSnGe3O12: Cr3+ with ultra thermal stability and high internal quantum yield (IQY) are explored. The emission intensity remained at 91.75 %@423 K of room temperature and the IQY/EQE reaches 91.41 %/34.04 % after doping the H3BO3 flux agent for optimization. Moreover, a single luminescence center was identified by cryogenic photo luminescence spectroscopy (PL) from 4 K to 300 K and lifetime decay curve. Importantly, NIR light can easily removes all kinds of background interference that cannot be easily eliminated by visible light, which is not only widely used in night vision and biological tissue penetration, but also has higher application prospects in NIR fingerprint detection. Moreover, it clearly displays high level of fingerprint details under high temperature conditions and after water immersion, thus it is perfectly suited for using in Automatic Fingerprint Identification System (AFIS) systems. Therefore, Ca3MgSnGe3O12: Cr3+ NIR pc-LEDs have great potential for latent fingerprint detection at actual crime scenes.

DACTYLOGRAPHY: The Scientific Study of Fingerprint

Fingerprint analysis has become important because of its reliability and scientific explanation in the courts. Fingerprint has three principles classification, uniqueness and identification. Now there are digital biometric scanner has the ability to access by fingerprint within a minute. This Automated Fingerprint Identification System has made easier to accessible. But the reliability would be little less than other ways to identify fingerprints. Because due to the malfunction of the scanner database can be manipulated or hacked by anyone to access the security. There are various types of fingerprints which needed to know for analyzing it. These types are 1. Patent Fingerprint(visible) 2. Latent Fingerprint(invisible) 3. Plastic/3d; to developing fingerprints there are different methods for different types of fingerprint. Fingerprint experts tend to develop fingerprint in crime scene for solving the crime. Whereas fingerprint become most important physical evidence in matter of solving crime. For developing to analyzing fingerprint expertise would be necessary for establishment. But there are various challenges for forensic experts which are distort prints, smudge prints, contaminated crime scene, database error, partial prints, etc. for the recording of the fingerprint on the wall also be challenging for experts which only can be recorded by photography. Henry classification system would be time consuming to evaluate the recorded fingerprints. Then there is chemical method to develop fingerprint due to the secretion of sweat, oil and other body fluids which make an invisible print. The outermost skin layers have various pores through where body fluid tend to secrets which is responsible for making fingerprints. To make it visible various chemical need to apply so that the secreted body fluid will react with chemical and its visibility will appear. There are some pattern types which tend to administer the individuality such as Arch, Loop and Whorl. Every pattern in itself is unique which can’t be match with any other person. Another way is ridge characteristics which is also valuable way to identify fingerprint. Because of its unique characteristics which tend to differ for every person and not only one characteristics but also 14 ridge characteristics are possible to identify through fingerprint. Porescopy and Edgescopy these are other ways to identify fingerprints but it is not practicable in India right now. There are basic two ways to develop latent fingerprints powder method and chemical methods. Through powder method, various powder and brushes will be need to develop fingerprints but it need to be know the surface where fingerprints are found. In chemical method there would be various chemical compound which needed to apply for the development of the latent fingerprints

Fingerprint Identification System based on VGG, CNN, and ResNet Techniques

Fingerprint Identification System based on VGG, CNN, and ResNet Techniques

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.

Forensic Fingerprint Analysis

Fingerprint evidence found at crime scenes provides vital impressions left when these skin secretions touch surfaces clues in serial criminal investigations. A fingerprint identification system employing deep machine learning and Convolutional Neural Networks (CNNs) could automate the analysis process. Images obtained from various physical and chemical crime scene investiga2tion techniques are entered into the database. However, partial latent prints lifted from scenes are often difficult to classify. The system operates in three phases: preprocessing fingerprint images, feature extraction, and matching. Preprocessing enhances image quality before feature extraction identifies distinctive minutiae points - ridge endings and bifurcations. False minutiae removal further refines the data. The preprocessed fingerprint data serves as input to train and test the CNN model. As the system persist due to the immutable individuality of fingerprint ridge arrangements [5]. While criminals attempt concealment, fingerprint traces stubbornly remain where other evidence would dissipate [6]. Without these durable biometric markers, crime scenes would often lack the critical traces needed to connect acts to perpetrators [7]. Latent prints lifted from crime scenes first undergo photographic documentation and chemical enhancement techniques in order to visualize trace details [8]. Computer analysis then further improves clarity, isolating minute identifying features known as minutiae [9]. Algorithmic extraction of differentiating traits classifies new latent prints, it continuously incorporates the prints enables training of automated comparison systems using along with confirmed suspect identity matches to improve accuracy. Automated classification and matching facilitate identification. The approach scales as the database grows in size without proportionate growth in human effort. Rapid fingerprint evidence analysis accelerates investigations, potentially solving more crimes by linking serial cases through a central digital repository. I aimed to restate the key technical ideas and flow using alternative vocabulary and phrasing while preserving semantic meaning. Please let me know if you need any clarification or have additional requirements for rephrasing the passage.

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.

Fingerprint Reconstruction: Approaches to Improve Fingerprint Images

Fingerprint reconstruction methods have been initially proposed to spoof the fingerprint identification systems, wherein the fingerprints are generated from the fingerprint features stored in the database for template matching/identification purpose. The reconstructed fingerprints attempt to validate in the absence of the user/person. The poor fingerprint Images with scratches on fingerprint image or latent fingerprints or overlapping fingerprints shall also be reconstructed for personality identification. In this paper we discuss the two fingerprint reconstruction methods, one which uses minutiae features for reconstruction and the other one uses deep learning methods to reconstruct the fingerprint images. The poor fingerprint image which fails to validate the identity due to various reasons like poor skin condition/large cuts on the fingers/wet fingers/poor scanning of images shall be reconstructed for increasing the matching accuracy. The requirement of performance measure parameters used for evaluation of these systems are equal error rate, false acceptance rate, false rejection rate and average matching score. The deep learning methods are more suitable for reconstructing the fingerprint images that appear damaged due to poor skin condition/large cuts on the fingers/wet fingers/poor scanning of images. In terms of matching score comparison, the deep learning methods have matching scores in between 23-94% whereas for minutiae-based techniques the matching score is between 82 and 99.99%. The other performance parameter is the equal error rate (ERR) required to meet has to be closer to 0. The matching score is computed with the assumptions of false acceptance rate (FAR) ranging from 1% to 0%.

Research Paper on Fingerprint Identification

Fingerprint identification is one of the most popular and reliable personal biometric identification methods. This paper describes an on-line fingerprint identification system consisting of image acquisition, edge detection, thinning, feature extractor, and classifier. The preprocessing part includes steps to acquire binarized and skeletonized ridges, which are needed for feature point extraction. Feature points (minutia) such as endpoints, bifurcations, and core points are then extracted, followed by false minutia elimination. Human fingerprints are rich in details called minutiae, which can be used as identification marks for fingerprint verification. The goal of this project is to develop a complete system for fingerprint identification.

Improving Sensor Interoperability between Contactless and Contact-Based Fingerprints Using Pose Correction and Unwarping

Current fingerprint identification systems face significant challenges in achieving interoperability between contact-based and contactless fingerprint sensors. In contrast to existing literature, we propose a novel approach that can combine pose correction with further enhancement operations. It uses deep learning models to steer the correction of the viewing angle, therefore enhancing the matching features of contactless fingerprints. The proposed approach was tested on real data of 78 participants (37,162 contactless fingerprints) acquired by national police officers using both contact-based and contactless sensors. The study found that the effectiveness of pose correction and unwarping varied significantly based on the individual characteristics of each fingerprint. However, when the various extension methods were combined on a finger-wise basis, an average decrease of 36.9% in equal error rates (EERs) was observed. Additionally, the combined impact of pose correction and bidirectional unwarping led to an average increase of 3.72% in NFIQ 2 scores across all fingers, coupled with a 6.4% decrease in EERs relative to the baseline. The addition of deep learning techniques presents a promising approach for achieving high-quality fingerprint acquisition using contactless sensors, enhancing recognition accuracy in various domains.

TEXTURE BASED FINGER PRINT IDENTIFICATION

In recent days , many digital finger print image based identification applications are used widely for human identity . Finger print features like minutiae (ridge lines, bifurcation, ridge ending) delta point, core point, texture patterns of ridges and valleys etc., are used for finger print identification. Reliable finger print identification system uses the finger print features to provide higher accuracy . Many finger print identification systems have been proposed based on minutiae , ridge lines. But these systems performance are not effective as compared to texture based methods. In this article , significance of feature extraction techniques , various texture based finger print identification methods are investigated and analysed . Limitations of minutiae, , and based identification systems are discussed. Also how effective the texture based identification systems are discussed. A comparative analysis of experimental results of various methods of finger print identification is done. This paper is concluded with the finding of appropriate method of texture based feature extraction for effective identity authentication. Keywords : Finger print feature, Texture feature, feature extraction, ridges, finger print recognition.

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.