Latent Fingerprint Recognition Research Articles

Meticulously designed red emission carbon dots as efficient and stable luminescent species for rapid latent fingerprint recognition and light emitting diodes

The preparation of carbon dots (CDs) emitting long wavelength has attracted more attentions due to their unique advantages in biological and optoelectronic fields, including the wide absorption, long PL lifetime and deep-tissue penetration. Herein, we prepare the nitrogen and sulfur co-doped red emission carbon dots (R-CDs) from the 2,4-diaminodiphenylamine (DDA) and thiourea via a solvothermal reaction, which exhibit reliable red emission at 613 nm and possess a high quantum yield (QY) of 34 %. Meanwhile, the structural analysis and theoretical calculation reveal that the sulfur doping from thiourea increases the nitrogen and oxygen content of CDs, leading to the red emission observed in R-CDs. Subsequently, we design an integrated fingerprints recognition system based on the R-CDs phosphors for the imaging of latent fingerprints (LFPs) and fast LFPs recognition, expanding the application of biometric identification and comparison technology based on fluorescence. Finally, we successfully apply the R-CDs into red and white light-emitting diodes, improving plant growth efficiency. As a result, this work provides a new in-depth insight of red emission CDs and expands the application in latent fingerprint recognition and light-emitting diodes.

Bio-waste derived multifunctional carbon dots for white light generation, forensic and antibacterial applications

The synthesis of multi-colour emitting solid-state fluorescent (SSF) carbon dots (CDs) is a challenging task due to the phenomenon of aggregation-induced self-quenching. However, this study presents an efficacious method to synthesize CDs from the sap stain of the cupressus lusitanica tree (cl-CDs) via a simple one-step microwave treatment. The resulting SSF CDs exhibited a particle size of approximately 3 nm, high stability, and remarkable efficacy in light conversion when coated on a UV light emitting diode (UV LED). The ensuing coating generated white light with CIE colour coordinates of (0.33, 0.34) and a high luminescence efficiency of approximately 671 L/W. The fluorescence capabilities exhibited by the cl-CDs in response to 254 nm and 365 nm UV light excitation make them an ideal choice for developing fluorescent inks to prevent counterfeiting. Moreover, the study investigated the notable fluorescence properties of cl-CDs as a luminescent fingerprint powder for the recognition of latent fingerprints on various surfaces. Additionally, the antibacterial potential of cl-CDs was evaluated against gram-positive and gram-negative bacteria, where the cl-CDs were utilized as an antibacterial dusting powder for fluorescent imaging of latent fingerprints on different substrates. Therefore, we believe that our present work offers a plethora of exciting possibilities for the multifunctional application of SSF green CDs with significant implications in white light generation, counterfeiting prevention, forensic applications, and healthcare.

Harnessing fluorescent carbon quantum dots from natural resource for advancing sweat latent fingerprint recognition with machine learning algorithms for enhanced human identification.

Nowadays, it is fascinating to engineer waste biomass into functional valuable nanomaterials. We investigate the production of hetero-atom doped carbon quantum dots (N-S@MCDs) to address the adaptability constraint in green precursors concerning the contents of the green precursors i.e., Tagetes erecta (marigold extract). The successful formation of N-S@MCDs as described has been validated by distinct analytical characterizations. As synthesized N-S@MCDs successfully incorporated on corn-starch powder, providing a nano-carbogenic fingerprint powder composition (N-S@MCDs/corn-starch phosphors). N-S@MCDs imparts astounding color-tunability which enables highly fluorescent fingerprint pattern developed on different non-porous surfaces along with immediate visual enhancement under UV-light, revealing a bright sharp fingerprint, along with long-time preservation of developed fingerprints. The creation and comparison of latent fingerprints (LFPs) are two key research in the recognition and detection of LFPs, respectively. In this work, developed fingerprints are regulated with an artificial intelligence program. The optimum sample has a very high degree of similarity with the standard control, as shown by the program's good matching score (86.94%) for the optimal sample. Hence, our results far outperform the benchmark attained using the conventional method, making the N-S@MCDs/corn-starch phosphors and the digital processing program suitable for use in real-world scenarios.

Multi-Surface Multi-Technique (MUST) Latent Fingerprint Database

Latent fingerprint recognition involves acquisition and comparison of latent fingerprints with an exemplar gallery of fingerprints. The diversity in the type of surface leads to different procedures to recover the latent fingerprint. The appearance of latent fingerprints vary significantly due to the development techniques, leading to large intra-class variation. Due to lack of large datasets acquired using multiple mechanisms and surfaces, existing algorithms for latent fingerprints enhancement and comparison may perform poorly. In this study, we propose a Multi-Surface Multi-Technique (MUST) Latent Fingerprint Database. The database consists of more than 16,000 latent fingerprint impressions from 120 unique classes (120 fingers from 12 participants). Including corresponding exemplar fingerprints (livescan and rolled) and extended gallery, the dataset has nearly 21,000 impressions. It has latent fingerprints acquired under 35 different scenarios and additional four subsets of exemplar prints captured using live scan sensor and inked-rolled prints. With 39 different subsets, the database illustrates intra-class variations in latent fingerprints. The database has a potential usage towards building robust algorithms for latent fingerprint enhancement, segmentation, comparison, and multi-task learning. We also provide annotations for manually marked minutiae, acquisition PPI, and semantic segmentation masks are also provided. We also present the experimental protocol and the baseline results for the proposed dataset. We assert that the availability of the proposed database can encourage research in handling intra-class variation in latent fingerprint recognition.

Synthesis of High-Fluorescent Diphenyl-anthracene Derivatives and Application in Detection of Nitroaromatic Explosives and Fingerprint Identification.

The development of high-intensity fluorescent materials is always the focuses and forefront projects because of their important applications in displays, sensing and detection fields. In recent years, the detection of explosives has attracted increasing attention due to security and counterterrorism issues. Herein, two diphenyl-anthracene (DPA) derivatives were designed and synthesized by introducing strong electron withdrawing fluorine atoms and cyano-groups to DPA, which exhibited strong fluorescence both in the solution and solid phase with the absolute quantum yields up to 70.4 % and 45.9 % respectively. The detection behavior of nitroaromatic explosives such as picric acid (PA), 2,4,6-trinitrotoluene (TNT) and 3-Nitropropionic acid (3-NP) also shows good sensitivity with the quenching constant as high as 6.3×104 L mol-1 . Theoretical calculation demonstrates that the fluorescence quenching behavior of the two DPA derivatives is caused by the behavior of photoinduced electron transfer (PET) and the resonance energy transfer (RET) studies explained the higher sensitivity and selectivity of both compounds towards PA than other nitro-containing explosives. Furthermore, the strong solid-state fluorescence of the DPA derivatives also shows excellent advantages in enhancing latent fingerprint recognition.

Structural characterization, luminescence properties and applications of Eu3+ ion-doped bismuth molybdate phosphor via a molten salt-assisted approach

A molten salt-assisted method for the synthesis of Eu[Formula: see text]-doped bismuth molybdate (BMO: Eu[Formula: see text]) phosphor was proposed. (BMO: Eu[Formula: see text]) phosphors with different phase structures were obtained by changing the calcination temperature. The applications in LED devices and latent fingerprint recognition of the obtained product were explored. The results of structural characterizations and scanning electron microscope (SEM) photographs analysis prove that the products exhibit different crystal structures and morphologies with the change of calcination temperature. Based on the above findings, the as-prepared products have potential applications in the field of WLED and potential fingerprint recognition.

Novel development of zinc oxide-coated carbon nanoparticles from pineapple leaves using sol gel method foroptimal adsorption of Cu2+ and reuse in latent fingerprint application.

This study underlines a latest approach of preparing nitrogen carbon nanoparticles fused on zinc oxide nanoparticle nanocomposite (N-CNPs/ZnONP nanocomposite) for the uptake of copper ions (Cu2+) from wastewater using a sol gel method. The metal loaded adsorbent was then applied in the latent fingerprint application. N-CNPs/ZnONP nanocomposite proved to be a good sorbent for the optimal adsorption of Cu2+ at pH 8 and 1.0g/L dosage. Langmuir isotherm best fitted the process with the maximum adsorption capacity of 285.71mg/g that was superior to most values reported in other studies for the removal of Cu2+. At 25°C, the adsorption was spontaneous and endothermic. Furthermore, Cu2+-N-CNPs/ZnONP nanocomposite revealed to be sensitive and selective for latent fingerprint (LFP) identification on a variety of porous surfaces. As a result, it is an excellent identifying chemical for latent fingerprint recognition in forensic science.

Latent Fingerprint Recognition: Fusion of Local and Global Embeddings

Latent Fingerprint Recognition: Fusion of Local and Global Embeddings

Latent Fingerprint Recognition using Hybrid Ant Colony Optimization and Cuckoo Search

Latent fingerprints are adapted as prominent evidence for the identification of crime suspects from ages. The unavailability of complete minutiae information, poor quality of impressions, and overlapping of multi-impressions make the latent fingerprint recognition process a challenging task. Although the contributions in the field are efficient for determining the match, there is a requirement to ameliorate the existing techniques as false identification can put the benign behind bars. This research work has amalgamated the Cuckoo Search (CS) algorithm with Ant Colony Optimization (ACO) for the recognition of latent fingerprints. It reduces the demerits of the individual cuckoo search algorithm, such as the probability of falling into local optima, the inefficient creation of nests at the boundary due to random walk and Levy flight attributes. The positive feedback mechanism of ant colony optimization makes it easy to combine with other techniques, reducing the risk of local failure and evaluating the global best solution. Prior to the evaluation of the proposed amalgamated technique on the latent fingerprint dataset of NIST SD-27, it is tested with the benchmark functions for different shapes and physical attributes. The benchmark testing and latent fingerprint evaluation result in the betterment of the amalgamated technique over the individual cuckoo search algorithm. The state-of-the-art comparison indicates that the amalgamation technique outperformed the other fingerprint matching techniques.

Nanocomposites of nitrogen-doped carbon dots/hydrotalcite with enhanced solid-state fluorescence for the recognition of latent fingerprints.

In this work, a green emissive nanocomposite was synthesized by embedding nitrogen-doped carbon dots into hydrotalcite (N-CD/hydrotalcite) via a hydrothermal process for the recognition of latent fingerprints (LFPs). Good dispersion of the nano-sized N-CD on the surface of hydrotalcite overcomes the fluorescence quenching of N-CD in the solid state and can enhance solid-state fluorescence. N-CD/hydrotalcite emits stable strong green fluorescence even at different excitation wavelengths and exhibits good selectivity and sensitivity for the visualization of LFPs on various substrates such as glass slides, tiles, leather, aluminum foil, printing paper, colored surfaces of plastic packing, copper foil, planks, leaves, currencies, and bar codes. The high-level details of the ridge patterns of both fresh and aged LFPs can be clearly identified with good clarity and high contrast without background interferences under the excitation of a 450 nm light source. There is no significant difference in the LFP image visualized by N-CD/hydrotalcite and commercial fluorescent powders, indicating that the effectiveness of N-CD/hydrotalcite for the visualization of LFPs is equivalent to that of commercial fluorescent powders. These observations illustrate that N-CD/hydrotalcite has great potential in the recognition of LFPs.

Magnetic modification of lanthanide-based upconversion nanocrystals for fingerprint information recognition

The development of new magnetic fluorescent materials is of great significance for identification and criminal investigation. Since the photosensitive elements used in conventional cameras have exhibited the highest quantum efficiency in the range of 500–700 nm, lanthanide-based upconversion nanoparticles (UCNPs) with main emission peaks at 507–533 nm, 533–568 nm and 637–683 nm are suitable for constructing magnetic fluorescent materials. In this work, we demonstrate a type of magnetic upconversion nanoparticle (MUCNP) of NaGdF<sub>4</sub>:Yb,Er-Fe<sub>3</sub>O<sub>4</sub> by a ligand-linked method. After optimizing the reaction parameters, the composite particles possess remarkable magnetic properties and upconversion fluorescence intensity and achieve high contrast for latent fingerprint recognition on various substrates. The combination of upconversion luminescence and magnetism contributes to good fingerprint recognition sensitivity and universality.

In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array

Detection and recognition of latent fingerprints play crucial roles in identification and security. However, the separation of sensor, memory, and processor in conventional ex-situ fingerprint recognition system seriously deteriorates the latency of decision-making and inevitably increases the overall computing power. In this work, a photoelectronic reservoir computing (RC) system, consisting of DUV photo-synapses and nonvolatile memristor array, is developed to detect and recognize the latent fingerprint with in-sensor and parallel in-memory computing. Through the Ga-rich design, we achieve amorphous GaOx (a-GaOx) photo-synapses with an enhanced persistent photoconductivity (PPC) effect. The PPC effect, which induces nonlinearly tunable conductivity, renders the a-GaOx photo-synapses an ideal deep ultraviolet (DUV) photoelectronic reservoir, thus mapping the complex input vector into a dimensionality-reduced output vector. Connecting the reservoirs and a memristor array, we further construct an in-sensor RC system for latent fingerprint identification. The system maintains over 90% recognition accuracy for latent fingerprint within 15% stochastic noise level via the proposed dual-feature strategy. This work provides a subversive prototype system of DUV in-sensor RC for highly efficient recognition of latent fingerprints.

An effective latent fingerprint enhancement and recognition system using dictionary learning and LCPnet mechanisms

Latent fingerprint recognition plays an essential role for law enforcement agencies to detect criminals and security purposes. One of the key stages utilized in the latent fingerprint recognition model is to automatically learn consistent minutiae from fingerprint images. However, the existing state-of-the-art recognition approaches are not adequate since live-scan fingerprint images and enhancements are necessary for each step of the recognition process. Hence, an automated recognition system along with appropriate minutiae learning algorithm is required for matching the latent fingerprint exactly. In this paper, an efficient recognition system using dictionary learning and Local Context-Perception deep neural network (LCPnet) has been proposed to enhance the accuracy of latent fingerprint recognition. Primarily, the Total Variation decomposition model is utilized to remove the smooth background noise and dictionary learning contributes to the extraction of multiple patches. Afterward, the LCPnet is trained for 12 patch types to develop a salient minutiae descriptor where every descriptor is trained using LCPnet with a particular patch size at a location surrounding the minutiae. The proposed detection system has been tested through two latent public datasets. Here, three different types of templates (LCPnet minutiae, LCPnet texture, and LCPnet minutiae+texture) are analyzed for evaluating the proposed fingerprint detection system. The performance results manifest that the proposed system acquires a superior recognition accuracy of 99.44% and 99.58% under two different datasets.

Enhanced convnet based Latent Finger Print Recognition

Latent finger print recognition plays an important role in forensic, criminal cases etc. The latent images will not be recognised easily since they are impartial images, which find difficult to match with the registered database. Due to noisy images, it is very difficult for recognition. Autoencoder plays an important role in pre-processing the latent image. ConvNetbased method is an efficient approach used for latent image recognition. For each minutiae extraction, ConvNet descriptor is performed. Both minutiae and texture matcher is considered for comparison. This technique is compared with existing methods which shows, that the proposed method provides a higher accuracy than the existing methods like CNN, skeleton approach nonlinear mapping and product quantization. The proposed method provides an accuracy of 76.4%, 80.4% and 86.4% for rank1,5 and 10 respectively.

A styryl-benzothiadiazole derivative with aggregation-induced enhanced emission for latent fingerprint recognition

An aryloxy- and a biphenyl-substituted styryl-benzothiadiazole derivatives were designed and synthesized aiming aggregation-induced enhanced emission (AIEE). In solutions, compounds presented absorption in the blue region (404–421 nm) and fluorescence emission ranging from 503 to 578 nm. Moderate to high Stokes-shifts along with marked positive solvatochromism were observed for both compounds. The biphenyl derivative (5) has not displayed AIEE. In contrast, the aryloxy-substituted compound (4) presented enhancement of luminescence (I/I0 up to 5.7) in acetone solutions containing fraction of water (ƒw) from 65% up to >99%. The presence of nanoaggregates in these conditions were corroborated by dynamic light scattering measurements and via temperature-dependency of fluorescence. Finally, the luminescent nanoaggregates of 4 were applied in latent fingerprint recognition allowing their detection in glass and aluminum surface, with good differentiation between ridges and furrows as well as visualization of secondary level information (core, independent ridges, bifurcations, ridges terminations, ridges dots).

Versatile core–shell magnetic fluorescent mesoporous microspheres for multilevel latent fingerprints magneto‐optic information recognition

AbstractLatent fingerprints are extremely vital for personal identification and criminal investigation, and potential information recognition techniques have been widely used in the fields of information and communication electronics. Although physical powder dusting methods have been frequently employed to develop latent fingerprints and most of them are carried out by using single component powders of micron‐sized fluorescent particles, magnetic powders, or metal particles, there is still an enormous challenge in producing high‐resolution image of latent fingerprints at different backgrounds or substrates. Herein, a novel and effective nanoimpregnation method is developed to synthesize bifunctional magnetic fluorescent mesoporous microspheres for latent fingerprints visualization by growth of mesoporous silica (mesoSiO2) on magical Fe3O4 core and then deposition of fluorescent YVO4:Eu3+ nanoparticles in the mesopores. The obtained Fe3O4@mesoSiO2@YVO4:Eu3+ microspheres possess spatially isolated magnetic core and fluorescent shell which were insulated by mesoporous silica layer. Due to their small particle size of submicrometer scale, high magnetization and low magnetic remanence as well as the combined magnetic and fluorescent properties, the microspheres show superior performance in visual latent fingerprint recognition with high contrast, high anti‐interference, and sensitivity as well as good retention on multifarious substrates regardless of surface permeability, roughness, refraction, colorfulness, and background fluorescence interference, and it makes them ideal candidates for practical application in fingerprint visualization and even magneto‐optic information storage.image

Dual-mode luminescent multilayer core-shell UCNPs@SiO2@TEuTbB nanospheres for high-level anti-counterfeiting and recognition of latent fingerprints

Currently, illegal counterfeiting activities are increasing with the continuous developments of society and the fabrication of high security anti-counterfeiting techniques is becoming essential. The lanthanide-doped nanomaterials have great potential in anti-counterfeiting for their high luminescence intensity, narrow half-peak width and high stability. Herein, the dual-mode luminescent UCNPs@SiO2@TEuTbB nanomaterials have been successfully fabricated for anti-counterfeiting. Morphology, structure and luminesce properties of the synthetic samples have been investigated via TEM, XRD, FT-IR, XPS and fluorescence spectroscopy. The results present that the size of the synthetic materials is about 100 nm. The luminesce properties clearly show that the UCNPs@SiO2@TEuTbB nanomaterials can emit strong UC red color under the NIR light. Meanwhile, these materials can emit several DC luminescence colors by adjusting Eu3+ ions concentration under UV lamp. Furthermore, the lanthanide-doped nanomaterials are widely used in recognition of LFPs providing valuable evidence in forensic filed. As a result, the images of LFPs developed by the as prepared materials on various surfaces under UV lamp can be well visualized. The three levels details of fingerprints are clearly visible with high sensitivity, selectivity and contrast. Thus, these results confirm that the synthetized UCNPs@SiO2@TEuTbB nanospheres can provide a foundation for high-level anti-counterfeiting and the detection of fingerprints.

Sc dopant induced tailoring of persistent luminescence in Na3YSi3O9:Eu2+ for information recording

A novel green afterglow Na3(Y,Sc)Si3O9:Eu2+ phosphor is synthesized for optical data storage and latent fingerprint recognition.

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.