Latent Fingerprint Recognition Research Articles

A facile construction of bifunctional core-shell magnetic fluorescent Fe3O4@YVO4:Eu3+ microspheres for latent fingerprint detection

Latent fingerprint recognition technique has received increasing attention because it helps to precisely identify human information for many applications. In this study, bifunctional core-shell magnetic fluorescent microspheres have been synthesized via a facile interface Pechini-type sol-gel method using citric acid and polyethylene glycol as chelating agent and cross-linking agent, respectively. The obtained Fe3O4@YVO4:Eu3+ microspheres possess a typical core-shell structure, large magnetization, and strong fluorescence emission. The surface morphology and roughness of the microspheres can be flexibly tuned by controlling the multistep interface deposition process and subsequent calcination temperatures. Due to their well-integrated bifunctionalities, these magnetic fluorescent microspheres show outstanding performance in the visualization of latent fingerprints on various substrates with high definition and excellent anti-interference, and therefore they have great potential for application in identity recognition.

Automatic Segmentation and Enhancement of Latent Fingerprints Using Deep Nested UNets

Latent fingerprints are one of the most important evidences used to identify criminals in the law enforcement and forensic agencies. Automated recognition of latent fingerprints is still challenging due to their poor image quality caused by unclear ridge structure and various overlapping patterns. Segmentation and enhancement are important to identify valid fingerprint regions, reduce the noise and improve the clarity of ridge structure for more accurate fingerprint recognition. In this paper, we propose a deep convolutional neural network architecture with the nested UNets for automatic segmentation and enhancement of latent fingerprints. First, to prepare training data, we synthetically generate the latent fingerprints and their segmentation and enhancement ground truth data for training. Then, a deep architecture of nested UNets is proposed to transform low-quality latent image into the segmentation mask and high-quality fingerprint through the pixels-to-pixels and end-to-end training. Finally, the test latent fingerprint is segmented and enhanced with the deep nested UNets to improve the image quality in one shot. The enhancement network is optimized by combining the local and global losses, which not only helps reconstruct the global structure, but also enhance the local ridge details of latent fingerprints. The proposed network can make use of multi-level feature maps in a pyramid way of nested UNets for segmentation and enhancement. Experimental results and comparison on NIST SD27 and IIITD-MOLF latent fingerprint databases demonstrate the promising performance of the proposed method.

Dual-Mode, Color-Tunable, Lanthanide-Doped Core–Shell Nanoarchitectures for Anti-Counterfeiting Inks and Latent Fingerprint Recognition

With the rapid development of information in modern society, the research and development of advanced anti-counterfeiting technology is becoming more and more important to protect the security and comprehensiveness of information. Therefore, fluorescent ink as an anti-counterfeiting technology and fingerprint recognition technology as a ″human information identification card″ has attracted the attention of many research groups. Herein, dual-mode (upconversion and downconversion) lanthanide-doped luminescent nanoarchitectures were developed using Y2O3:Er3+,Yb3+ nanoparticles as a core and layered lanthanide hydroxides nanomaterials as a shell. Under the irradiation of 980 nm near-infrared light, the nanoarchitectures emitted a bright upconverted red light emission. Meanwhile, under the irradiation of 254 nm UV light, the nanoarchitectures can directly emit multicolor luminescence (from green to yellow-green, yellow, orange, and red) by changing the suitable ratios of Tb3+/Eu3+ ions. The information can only be extracted when the irradiation of two kinds of excitation light sources existed at the same time, which can improve the difficulty of illegal imitation and enhance the level of anti-counterfeiting. Furthermore, these luminescent nanoarchitectures were investigated for visual latent fingerprint recognition on various substrates with high definition, high sensitivity, and high anti-interference. These results indicated that the nanoarchitectures reported in this study may have great application prospects in information security and identity recognition.

Fluorescence of a Natural Fluorophore as a Key to Improve Fingerprint Contrast Image.

The optical and morphological properties of resveratrol were investigated. This nontoxic fluorescent natural material, emitting in the visible blue light, was used as an optical marker, enabling the enhancement of the image contrast coming from relief pictures marked on challenging surfaces. By applying appropriated imaging softwares, this marker was verified to be very useful in the latent fingerprint recognition deposited on different wood surface types, mainly those with high level of roughness, where conventional forensic materials do not allow effective fingerprint image visualization.

Recognition of Latent Fingerprints and Ink-Free Printing Derived from Interfacial Segregation of Carbon Dots.

Carbon dots (CDs) have attracted increasing interest in recent years owing to their desirable properties. Despite the availability of diverse elaborate CDs, the function and application of CDs are far to be fully exploited. Here, biomass-derived carbon dots dispersed in a polymer matrix are found to behave as ink-free patterned substrates, which are demonstrated to be useful for nondestructive collection and recognition of latent fingerprints (LFPs), as well as printing. The coating of CD/poly(vinyl alcohol) solution on a LFP yields a flexible transparent film; a stable fluorescent fingerprint with clear ridge details enabling personal identification is formed on this film. Encouragingly, this method can be applied to nondestructively lift and recognize long-timely exposed LFPs from various surfaces. The mechanism for LFP collection and visualization is proposed, which should be ascribed to the interfacial segregation of CDs in the polymer matrix during the film forming process. This mechanism is further validated by and utilized for application of CD/polymer composites in relief printing, intaglio printing, and micro-trace transferring.

Patch-based Segmentation of Latent Fingerprint Images Using Convolutional Neural Network

ABSTRACTLatent fingerprint segmentation involves marking out all the foreground regions accurately in a latent fingerprint image, but due to poor quality images and complex background, segmentation of latent fingerprint images is one of the most difficult tasks in automatic latent fingerprint recognition systems. In this article, we propose a patch-based technique for segmentation of latent fingerprint images, which uses Convolutional Neural Network (CNN) to classify patches. CNN has recently shown impressive performance in the field of pattern recognition, classification, and object detection, which inspired us to use CNN for this complex task. We trained the CNN model using SGD to classify image patches into fingerprint and non-fingerprint classes followed by proposed false patch removal technique, which uses “majority of neighbors” to remove the isolated and miss-classified patches. Finally, based on the final class of patches, an ROI is constructed to mark out the foreground from the background of latent fingerprint images. We tested our model on IIIT-D latent fingerprint database and the experimental results show improvements in the overall accuracy compared to existing methods.

The Recognition of Sweat Latent Fingerprints with Green-Emitting Carbon Dots.

The recognition of fingerprints has played an extremely important role in criminal investigations, due to its uniqueness. This paper reports on the recognition of sweat latent fingerprints using green-emitting, environment-friendly carbon dots prepared with DL-malic acid and ethylenediamine, and the exploration of impacting factors in the development process of fingerprints. The experiments showed that better fingerprint images could be obtained when the latent fingerprints are developed in green-emitting carbon dots with pH 9 for 30 min, at room temperature. The reported method was also effective for latent fingerprints on a variety of substrates, as well as for those water-immersed ones, where the developed fingerprint remained stable after long-term preservation. Furthermore, the fluorescent three-dimensional fingerprint image could provide direct and simple evidence on pressing habits. The objective of this paper was to present this method. The method may help to narrow the range of suspects during criminal investigations and in forensic science.

Multiple Criteria Decision Analysis Based Overlapped Latent Fingerprint Recognition System Using fuzzy Sets

Latent fingerprints have attracted considerable attention from researchers in the fields of forensics and law enforcement applications. Public demand for these applications may be the driving force behind further progress in biometrics research. Although great effort has been taken to devise algorithms for overlapped latent fingerprint classification system, there are still many challenging problems involved in fingerprint classification systems. Most of the fingerprint-based applications will prolong with fingerprint recognition because of its proven performance, the existence of large databases and the availability of the fingerprint devices with minimum cost. There are various issues that need to be addressed to develop fingerprint classification system. In this connection, there are some designing challenges such as nonlinear distortion, low-quality image, segmentation, sensor noise, skin conditions, overlapping, inter-class similarity, intra-class variations and template ageing. In crime scenes, the latent images can be overlapped with some background images or more number of fingerprint images from same person or different person. An overlapped fingerprint image should be processed for fingerprint classification. This proposed recognition system suggests multiple criteria decision analysis technique to assess the overlapped latent fingerprints. The proposed multiple criteria such as first-order, second-order and third-order features are used to classify the overlapped latent fingerprints. The proposed system designs the novel classification system for overlapped latent fingerprint images using ANFIS classifier. Extensive experiments are performed on the simultaneous latent fingerprint databases, and National Institute of Standards and Technology-Special Database 27, Fingerprint Verification Competition 2006 Database1-A and Database2-A databases. The planned work enables accurate and fast data retrieval by using one-to-N fingerprint classification for overlapped latent images. The experimental results are highly promising, and they outperform the existing systems in classifying overlapped images. The performance of adaptive neuro fuzzy inference system classifier is evaluated by applying the k-fold cross-validation technique. The outcome of the work shows that the overlapped fingerprint is classified in a successful manner, and the results are compared with Bayes, SVM and MLP classifiers. The obtained results show that the proposed system achieves a better classification rate of 90.66% with 5 s, 86.66% with 12 s compared to the existing system.

Automated Latent Fingerprint Recognition.

Latent fingerprints are one of the most important and widely used evidence in law enforcement and forensic agencies worldwide. Yet, NIST evaluations show that the performance of state-of-the-art latent recognition systems is far from satisfactory. An automated latent fingerprint recognition system with high accuracy is essential to compare latents found at crime scenes to a large collection of reference prints to generate a candidate list of possible mates. In this paper, we propose an automated latent fingerprint recognition algorithm that utilizes Convolutional Neural Networks (ConvNets) for ridge flow estimation and minutiae descriptor extraction, and extract complementary templates (two minutiae templates and one texture template) to represent the latent. The comparison scores between the latent and a reference print based on the three templates are fused to retrieve a short candidate list from the reference database. Experimental results show that the rank-1 identification accuracies (query latent is matched with its true mate in the reference database) are 64.7 percent for the NIST SD27 and 75.3 percent for the WVU latent databases, against a reference database of 100K rolled prints. These results are the best among published papers on latent recognition and competitive with the performance (66.7 and 70.8 percent rank-1 accuracies on NIST SD27 and WVU DB, respectively) of a leading COTS latent Automated Fingerprint Identification System (AFIS). By score-level (rank-level) fusion of our system with the commercial off-the-shelf (COTS) latent AFIS, the overall rank-1 identification performance can be improved from 64.7 and 75.3 to 73.3 percent (74.4 percent) and 76.6 percent (78.4 percent) on NIST SD27 and WVU latent databases, respectively.

EGCG assisted Y2O3:Eu3+ nanopowders with 3D micro-architecture assemblies useful for latent finger print recognition and anti-counterfeiting applications

In the present work, an intense red Y2O3:Eu3+ nanopowders (NPs) were synthesized by ultrasonication, hydrothermal and solution combustion methods. Structural and luminescence properties of obtained compounds were compared by using powder X-ray diffraction and photoluminescence (PL) studies respectively. Compounds obtained from ultrasonication method showed the smallest crystallite size of ∼10 nm and increased PL intensity as compared to compounds obtained by other two methods. Optimized NPs obtained from ultrasonication method were further used as a labeling agent for the better visualization of latent finger prints (LFPs) on different porous and non-porous surfaces. Visualized LFPs exhibits high sensitivity, reproducibility, selectivity, reliability and can obtain the complete ridge details. LFPs of up to level 3 ridge details were explored (i.e., sweat pores) by staining optimized Y2O3:Eu3+ NPs by simple powder dusting method. SEM micrographs of the samples obtained from ultrasonication method showed flower like morphology with crystal-splitting based particles. Images obtained using Y2O3:Eu3+ based fluorescent ink showed high intense light under UV excitation and no clear image under normal light confirms its potential as anti-counterfeit ink. The property of high surface activity demonstrates that the prepared NPs are the promising luminescent materials for display devices, LFPs visualization and anti-counterfeiting applications.

Latent Fingerprint Value Prediction: Crowd-Based Learning

Latent fingerprints are one of the most crucial sources of evidence in forensic investigations. As such, development of automatic latent fingerprint recognition systems to quickly and accurately identify the suspects is one of the most pressing problems facing fingerprint researchers. One of the first steps in manual latent processing is for a fingerprint examiner to perform a triage by assigning one of the following three values to a query latent: Value for Individualization (VID), Value for Exclusion Only (VEO), or No Value (NV). However, latent value determination by examiners is known to be subjective, resulting in large intra-examiner and inter-examiner variations. Furthermore, in spite of the guidelines available, the underlying bases that examiners implicitly use for value determination are unknown. In this paper, we propose a crowdsourcing based framework for understanding the underlying bases of value assignment by fingerprint examiners, and use it to learn a predictor for quantitative latent value assignment. Experimental results are reported using four latent fingerprint databases, two from forensic casework (NIST SD27 and MSP) and two collected in laboratory settings (WVU and IIITD), and a state-of-the-art latent automated fingerprint identification system (AFIS). The main conclusions of this paper are as follows: 1) crowdsourced latent value is more robust than prevailing value determination (VID, VEO, and NV) and latent fingerprint image quality for predicting AFIS performance; 2) two bases can explain expert value assignments, which can be interpreted in terms of latent features; and 3) our value predictor can rank a collection of latents from most informative to least informative.

A review study on latent fingerprint recognition techniques

Latent fingerprint has become more popular and attracted among the researchers in the recent days and it has been used widely in security such as India’s Aadhaar project, Department of Homeland Security’s US-VISIT program, the UK Border Agency, law enforcement and forensic applications. There have been different biometric traits that were used for matching to identify the person. While investigation a small number of accesses are recognized in latent fingerprint images because the fingerprint has own individuality. Latent fingerprint has been consisting some challenges in term of poor quality images, unclear texture and nonlinear distortion, appropriate matching algorithm and publicly available latent fingerprint database. Consequently, the study has considered and talked about different accessible strategies and methods that have been utilized as a part of the past exploration concentrates on furthermore gives the bearing to further research in the different field of inactive unique finger impression application.

Novel and highly efficient red luminescent sensor based SiO2@Y2O3:Eu3+, M+ (M+ = Li, Na, K) composite core–shell fluorescent markers for latent fingerprint recognition, security ink and solid state lightning applications

Facile solution combustion route was used to prepare the optimized composite core shell SiO2@Y2O3:Eu3+, Li+ fluorescent nanopowders (NPs) with superior fluorescence intensity under UV light. Powder dusting technique was used for the visualization of latent fingerprints (LFPs) on different porous and non-porous surfaces. The results display that the amorphous SiO2 microspheres were covered by crystalline Y2O3:Eu3+, Li+ NPs, resulting in spherical core-shell structure. The phosphors exhibit intense pure strong red emission corresponding to characteristic Eu3+ ions 5D0→ 7F2 transitions under NUV excitation. The CIE co-ordinates were found to be (x=0.63, y=0.36) which is very close to standard NTSC values (x=0.67, y=0.33). Judd-Ofelt theory was used to estimate the intensity parameters (Ω2 and Ω4) as well as radiative properties. The CCT value was∼3475K which was less than 5000K, as a result the phosphor was highly useful in warm light emitting diodes. Thus, results presented confirms that the developed method was simple, fast and optimized phosphor was effectively used for multifunctional applications namely luminescent sensor for visualization of LFPs, solid state lightning and security ink applications.

Latent Fingerprint Recognition using Hybridization Approach of Partial Differential Equation and Exemplar Inpainting

Objectives: Biometric based Fingerprint recognition system is one of the well-adapted approaches in online security prospects. However, due to user-friendly behaviour of advanced computing systems, biometric approach is not much in use now days. The approach left its footprints only with the applications to identify criminal activities at crime scenes where fingerprints are mainly available in latent form. Latent fingerprints are the accidently left finger skin impressions by criminals. These impressions are invisible for the naked human eye and usually captured with lasers, chemical, powders etc. These captured latent fingerprints carries less minutiae information with distorted ridges and high level of pattern overlapping. So, it is not easy to identify the criminals with partial fingerprint information. Methods/Statistical Analysis: In this paper, hybrid approach of Exemplar In painting and Partial Differential Equation is used to fill up the distorted ridges. The main goal of this work is to present the framework to reconstruct the latent distorted fingerprint and further use them to find the best match for those enhanced reconstructed latent fingerprints. For the experimentation of proposed hybrid concept, IIIT Delhi latent and NIST SD-27 databases of fingerprint are used. In this experimentation, different fingerprint enhancement filters like Canny Edge Detection Filter, Prewitt filter, Laplacian Filter, Sobel Filter, Gaussian Low Pass Filter, Gaussian High Pass Filter are used. Findings: Different filters show different performance of dataset images for latent fingerprint recognition. The overall performance of the automated latent fingerprint identification approach is analysed in terms of false acceptance rate and genuine acceptance rate. Application/Improvements: In this way, latent fingerprint can be used for the recognition of criminal activities at crime scenes where fingerprints are mainly available in latent form. The overall concept shows better results for canny filter as compare to other considered filters in enhancement.

Adaptive latent fingerprint segmentation using feature selection and random decision forest classification

Latent fingerprints are important evidences used by law enforcement agencies. However, current state-of-the-art for automatic latent fingerprint recognition is not as reliable as live-scan fingerprints and advancements are required in every step of the recognition pipeline. This research focuses on automatically segmenting latent fingerprints to distinguish between ridge and non-ridge patterns. There are three major contributions of this research: (i) a machine learning algorithm for combining five different categories of features for automatic latent fingerprint segmentation, (ii) a feature selection technique using modified RELIEF formulation for analyzing the influence of multiple category features on latent fingerprint segmentation, and (iii) a novel SIVV based metric to measure the effect of the segmentation algorithm without the requirement to perform the entire matching process. The image is tessellated into local patches and saliency based features along with image, gradient, ridge, and quality based features are extracted. Feature selection is performed to study the contribution of the various category features towards foreground ridge pattern representation. Using these selected features, a trained Random Decision Forest based algorithm classifies the local patches as background or foreground. The results on three publicly available databases demonstrate the efficacy of the proposed algorithm.

Frontispiece: High Excimer-State Emission of Perylene Bisimides and Recognition of Latent Fingerprints

Frontispiece: High Excimer-State Emission of Perylene Bisimides and Recognition of Latent Fingerprints

High excimer-state emission of perylene bisimides and recognition of latent fingerprints.

High excimer-state emission in the H-type aggregate of a novel asymmetric perylene bisimide derivative, 6, with triethyleneglycol chains and lactose functionalization was achieved in water. Furthermore, its application for enhancing the visualization of transfer latent fingerprints from glass slides to the poly(vinylidene fluoride) (PVDF) membrane was explored, which showed clear images of the latent fingerprint in daylight and under 365 nm ultraviolet illumination.

Advances in the development and component recognition of latent fingerprints

Fingerprints have been used as an indispensable tool for personal identification in forensic investigations since the late 19th century. At present, fingerprinting technology has moved away from its forensic roots and is incorporating a broader scientific range, e.g., material science, spectroscopy and spectral analysis, and even in vitro diagnosis. After a brief introduction to latent fingerprints, this mini-review presents the pioneering progresses of fingerprinting technologies including (i) material and electrochemical techniques, and (ii) spectral and spectroscopy imaging techniques and immunological techniques capable of both the visualization of a fingerprint and the detection of chemicals present in it. Finally, perspectives on this rapidly developing field are discussed.

Latent Fingerprint Matching: A Survey

Latent fingerprint has been used as evidence in the court of law for over 100 years. However, even today, a completely automated latent fingerprint system has not been achieved. Researchers have identified several important challenges in latent fingerprint recognition: 1) low information content; 2) presence of background noise and nonlinear ridge distortion; 3) need for an established scientific procedure for matching latent fingerprints; and 4) lack of publicly available latent fingerprint databases. The process of automatic latent fingerprint matching is divided into five definite stages, and this paper discusses the existing algorithms, limitations, and future research directions in each of the stages.

Aggregation induced emission for the recognition of latent fingerprints

The aggregation-induced emission of tetraphenylethene was explored for enhancing the visualization of latent fingerprints on wet non-porous surfaces.