Fingerprint Images Research Articles

50‐1: Invited Paper: Flexible Imager with Organic Photodetector for Sensing Applications

We developed a flexible imager comprising low‐temperature polycrystalline silicon thin‐film transistors and organic photodiodes. The developed flexible imager has high‐resolution and high‐speed readout characteristics. Owing to these characteristics, pulse wave signals can be measured, and the images of fingerprints and veins can be obtained. Furthermore, we developed three‐axis tactile pressure sensors by integrating flexible optical components. The sensors can detect both normal and tangential pressures.

Dual-mode excitable Vis/NIR-II luminescence of lanthanide-doped core@multishell nanostructures for anti-counterfeiting and imaging of latent fingerprints

The development of luminescent materials that can simultaneously realize multiple emissions can help improve the reliability of anti-counterfeiting and the security of encrypted information. Lanthanide-doped nanomaterials are very suitable for advanced anti-counterfeiting applications because of their abundant 4 f energy levels and near-infrared (NIR) laser excitability. Herein, we design lanthanide-doped core@multishell nanoparticles NaYF4:Yb,Er/Ho/Tm@NaYF4@NaYF4:Nd@NaYF4, which can realize upconversion luminescence of Er/Ho/Tm under 980 nm excitation, as well as non-overlapping NIR-II luminescence under 980 nm or 808 nm excitation. By precisely controlling the distribution of lanthanide ions in the core-shell layer, the luminescence interference of nanomaterials under different wavelength excitation can be avoided. Experiments show that multi-color security and information encryption under NIR laser excitation can be achieved using aqueous inks containing the designed multilayered core-shell nanostructures, which are covert and difficult to forge. And the multilayer core-shell nanopowder can be used for efficient identification of latent fingerprints on complex surfaces.

Fingerprint Image Compression using Biorthogonal and Orthogonal Wavelets at Different Levels of Discrete Wavelet Transform

Fingerprint Image Compression using Biorthogonal and Orthogonal Wavelets at Different Levels of Discrete Wavelet Transform

Fingerprint recognition using convolution neural network with inversion and augmented techniques

Fingerprints are considered as one of the most important and prominent feature for an individual identification. Due to their consistency and reliability in biometric feature identification, they are most widely used for biometric recognition systems. In these systems, the relevant feature extraction plays important role in achieving required classification accuracy. In recent time, deep learning techniques are being used for fingerprint recognition with more accuracy and efficient results. Major difficulty which has been reported in previous researches, is the limited size of samples. Therefore, we propose two approaches, inversion and multi augmentation to augment the sample size with newly generated images for each feature map. Besides this, multiple networks are used simultaneously for feature extraction from newly generated images in parallel mode. Deep neural network architectures are used with proposed inversion methods and multi augmentation methods to classify the samples of fingerprints for personnel identification and verification. Pre-trained deep convolutional models like VGG16, VGG19, ResNet50 and InceptionV3 are fine-tuned with new processed fingerprint images for feature extraction and classification. The collective samples of fingerprints have been classified into 10 classes. The simulation results have been obtained with different optimizers and it has been observed that VGG 19 model exhibits the accuracies of 88 % and 93 % with inversion and multi augmentation approaches respectively. Whereas, VGG16 model exhibits 93 % with inversion approach and 97 % with multi augmentation approach. Thus, the proposed approach exhibits the accuracy up to 97 % with VGG16 model which is significantly much higher than that of any other model with the same dataset FVC2000_DB4.

Multimodal Authentication System with Gender Classification

Multimodal authentication system which takes input as face, fingerprint and iris images and fusion of these images is done after the feature extraction. The fused images is used to authenticate the user. If the user is authenticated user then it also classifies the gender of the user

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.

Solvothermal Treatment of Micron-Sized Commercial SrAl2O4:Eu2+, Dy3+ Phosphors and One-Step Preparation of Nanophosphors for Fingerprint Imaging

Preparing submicron and nanoscale phosphors with good optical properties for practical applications is a challenging task for current inorganic long afterglow luminescent materials. This study utilized commercialized SrAl2O4:Eu2+, Dy3+ phosphors (SAOED) as raw materials and employed solvents with lower polarity or non-polar solvents for dynamic solvothermal treatment. The commercialized phosphor’s overall average particle size was reduced from 42.3 μm to 23.6 μm while maintaining the fluorescence intensity at 91.39% of the original sample. Additionally, the study demonstrated the applicability of the dynamic solvothermal method to most other commercialized inorganic phosphors. The experiment produced a high-brightness nano-sized phosphor with a yield of 5.64%. The average diameter of the phosphor was 85 nm, with an average thickness of 16 nm. The quantum efficiency of the phosphor was 74.46% of the original sample. The fingerprint imaging results suggest that the nano-sized phosphors have potential for practical applications.

Non-destructive and simultaneous development and enhancement of latent fingerprints on stainless steel based on the electrochromic effect of electrodeposited manganese oxides

Latent fingerprints, as one of the most frequently encountered traces in crime scene investigation and also one of the largest sources of forensic evidence, can play a critical role in determining the identity of a person who may be involved in a crime. Due to the invisible characteristic of latent fingerprints, exploring efficient techniques to visualize them (especially the ones resided on metallic surfaces) while retain the biological and chemical information (e.g., touch DNA) has become a multidisciplinary research focus. Herein we reported a new and highly sensitive electrochemical interfacial strategy of simultaneously developing and enhancing latent fingerprints on stainless steel based on synchronous electrodeposition and electrochromism of manganese oxides in a neutral aqueous electrolyte. By utilizing a specially designed device for electrochemical testing and image capture, a series of electrochemical measurements, physical characterization and image analysis have been applied to evaluate the feasibility, development accuracy and enhancement efficacy of the proposed electrochemical system. The qualitative and quantitative analysis on the in situ and ex situ fingerprint images indicates that the three levels of fingerprint features can be precisely developed and effectively enhanced. Forensic DNA typing has also been performed to reveal actual impact of the proposed electrochemical system on subsequent analysis of touch DNA in fingerprint residues. The ratio of detected loci after electrochemical treatment reaches up to 98.5 %, showing non-destructive nature of this fingerprint development and enhancement technique.

Eu3+ activated gadolinium oxide quantum dots: Biocompatible red luminescent phosphor for photonic, latent fingerprint imaging, and anticancer therapy

In this study, we employed a straightforward chemical reduction method to synthesize Gd2O3 quantum dots (QDs) doped with Eu3+ ions, yielding a remarkable red emission. X-ray diffraction (XRD) analysis confirmed the cubic structure of both pristine and Eu3+ doped Gd2O3 QDs. Characterization of particle size and elemental composition was accomplished through Transmission Electron Microscope (TEM) and X-ray photoelectron spectroscopy (XPS), respectively. Photoluminescence analysis under UV excitation revealed a blue emission at 436 nm for the undoped Gd2O3 QDs. Furthermore, room temperature photoluminescence analysis was conducted on the undoped Gd2O3 quantum dots (QDs) with a UV excitation source. While Gd2O3 QDs emitted a blue light at 436 nm under 396 nm illumination, the Eu3+ doped Gd2O3 QDs exhibited a striking red luminescence under the same 394 nm wavelength. The red luminescence was notably enhanced in Gd2O3 QDs containing 0.5 mol% Eu3+ ions, although concentration quenching was observed when the concentration exceeded this level. A transfer of energy from pristine Gd2O3 QDs to Eu3+ ions was observed, leading to color purity exceeding 80.8% in the optimized sample. Notably, the 0.5 mol% Eu3+-doped Gd2O3 QD phosphor displayed non-cytotoxicity on C2C12 muscle myoblast cells while demonstrating remarkable cytotoxicity against MDA-MB-231 human breast cancer cells. This versatile material holds great promise for applications in latent fingerprinting, security coding, labeling, anticancer therapies, bio-imaging, and the advancement of lanthanide-doped QD phosphors for a wide range of photonic and biomedical applications.

MINUTIA EXTRACTOR AND MINUTIA MATCHER BASED FINGER PRINT RECOGNITION USING DIGITAL IMAGE PROCESSING

The MATLAB code presents a user-friendly graphical interface for fingerprint recognition and verification. Through this interface, users can load fingerprint images and execute a series of image processing tasks aimed at enhancing image quality and extracting crucial features. Operations such as histogram equalization, FFT enhancement, binarization, and thinning are available to optimize the fingerprint images for analysis. The system facilitates minutia extraction, identifying significant points like ridge endings and bifurcations, which are fundamental for fingerprint matching. Moreover, it includes functionalities for removing spurious minutiae, thereby improving the accuracy of the recognition process. Users can save and load fingerprint templates, streamlining the storage and retrieval of extracted features for matching purposes. Additionally, the system supports fingerprint matching, allowing users to compare templates and determine their similarity or match percentage. This versatile tool finds applications in biometric authentication systems, forensic analysis, and security domains, where robust fingerprint recognition and verification are imperative. Overall, the MATLAB code provides a comprehensive framework encapsulated within a user-friendly interface, offering researchers and practitioners a valuable resource for fingerprint analysis and biometric authentication. Keywords : Fingerprint recognition, GUI (Graphical User Interface), Security systems, Access control, Forensic analysis, User-friendly

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.

Sustainable latent fingerprint enhancement with ink-free printing and shape memory behavior using Parthenium Hysterophorus-derived carbon dots

We developed a method to produce red fluorescent carbon dots from Parthenium hysterophorus leaves (PCDs) through hydrothermal carbonization. These PCDs exhibit consistent photoluminescence, emitting light regardless of excitation, with a 6.01 ns fluorescence lifetime and a 10.2% quantum yield. They possess a fine size of 4.4 nm, excellent hydrophilicity, and stability in a pH range of 4–11. Leveraging their optical properties, these PCDs function as precise nano-probes for detecting Fe3+ ions, demonstrating a linear correlation within 0 to 50 μM concentrations and a low detection limit of approximately 0.14 μM. Emitting light at about 620 nm, these PCDs remain visible even in daylight. When applied to fingerprints on various substrates and dried, they reveal distinct prints under 365 nm UV light, retaining stability for 24 h. This resilience is attributed to preventing fluorescence reduction, achieved through the coffee-ring effect and electrostatic interactions during drying. Employing deep learning with the YoLOv8 algorithm, fluorescence images of these latent fingerprints (LFPs) exceptionally resemble standard controls. These PCDs serve a dual role as fluorescent ink and nanocomposite films within a PVA matrix, demonstrating potential in anti-counterfeiting applications. This study presents a straightforward method for synthesizing PCDs with intense red luminescence, establishing them as a versatile platform for Fe3+ sensing, flexible fluorescent films, latent fingerprints, and anti-counterfeiting pursuits.

Cucurbituril‐Confined Tetracation Supramolecular 2D Organic Framework for Dual‐Emission TS‐FRET

AbstractHerein, cucurbituril‐regulated supramolecular multi‐dimensional organic framework constructed is reported by tetraphenylvinyl grafted bromophenylpyridine salt derivatives (TPE‐BrN) and cucurbit[n]uril (n = 6, 7, 8) via non‐covalent cross‐linked, giving supramolecular macrocycle‐confined fluorescence‐phosphorescence dual emission at 560 and 510 nm in solid state. Different from the formation of TPE‐BrN⊂CB[6] complexes and TPE‐BrN⊂CB[7] nanoparticle, TPE‐BrN⊂CB[8] manifested high‐efficient 2D network assembly. TPE‐BrN⊂CB[8] assembly can effectively activate triplet‐state to singlet‐state Förster resonance energy transfer (TS‐FRET) with long‐lived near‐infrared emission at 675 nm via doping organic dye Nile red (NiR) and the energy transfer efficiency reached up to 99%. Although CB[7] can also induce the typical phosphorescence emission at 510 nm, there is no TS‐FRET occurring after doping NiR into TPE‐BrN⊂CB[7] assembly due to the formation of different assembly modes. The multicolor long‐lived emission has been demonstrated very well based on the TS‐FRET process activated by CB[8] macrocyclic confined 2D organic framework, which can be successfully applied to fingerprint imaging and the construction of logic gate systems. It provides a novel method for supramolecular macrocycle confined phosphorescence regulation and the development of luminescent materials.

Fingerprint Identification with Fusion of Gabor and Minutiae Features Using BPNN Classifier

This study presents an innovative approach to fingerprint identification by leveraging the fusion of Gabor and Minutiae features, employing a Backpropagation Neural Network (BPNN) classifier for accurate categorization. The process involves initial handling of a fingerprint image dataset, including crucial pre-processing steps such as resizing images and implementing morphological operations like dilation, erosion, and opening. Subsequently, Gabor features and minutiae extraction are performed, followed by the fusion of these features to create a comprehensive representation. To address dimensionality concerns, Principal Component Analysis (PCA) is applied. The dataset, comprising the fused features and corresponding labels, is then loaded for the final step - classification using a BPNN. The network is configured for feed-forward backpropagation, distinguishing fingerprint patterns into categories such as arch, left loop, right loop, tented, and whorl. The evaluation metric used to measure the success of the classification process is accuracy. This approach aims to enhance fingerprint recognition by combining distinctive Gabor and minutiae features, ultimately achieving a more robust and precise identification system through the utilization of neural network-based classification. Keywords—BPNN, PCA, Finger Print Images Dataset

Fluorescent columnar liquid crystals based on BTD hexacatenars with salicylaldimine and benzaldimine core: Self-assembly and multifunctional properties

Two series of hexacatenars SB/n and BB/n containing a benzothiadiazole (BTD) central core with triple alkyl chains at both ends via salicylaldehyde or benzaldehyde derivated Schiff base links, respectively, were prepared. Different Schiff base links lead to different self-assembly and photophysical behaviors. Both series can self-assemble into columnar liquid crystal (LC) phases but with different lattices (p6mm for SB/n, p4mm for BB/n) in bulk states, and SB/n can additionally form gels in organic solution. Interestingly, SB/n series were characterized with excited state intramolecular proton transfer (ESIPT) and aggregation-induced emission enhancement (AIEE) effects, and displayed high fluorescence in their solid, LC and gel states with large Stokes shifts and environmental sensitive unique spectra patterns. The effect of photoisomerization driven by ESIPT process on LC state of SB/n was observed. The applications of SB/n as fingerprint imaging device, Al3+ fluorescence chemosensor, white light emitting diodes (WLED) as well as light-emitting liquid crystal displays (LE-LCD) have been successfully realized.

Fingerprint Recognition and Verification using Fourier Domain Filtering and Histogram Equalization Techniques

Fingerprint Recognition is a vital method in biometric identification and verification of human beings in various domains like Security, Digital Forensics, Internet of Things (IoT), and many more. Each individual human is having distinct fingerprint pattern than others, hence it is one of the most prominent and widely used method to distinguish individuals. Many research studies and solutions have been developed in biometric domain since a decade, which influences now in making the process of fingerprint recognition more optimized, faster and efficient. However, present fingerprint acquisition/recognition systems have some limitations, mainly longer computation time for fingerprint matching and evaluating the results. This paper presents a procedure for fingerprint matching that takes into account minutiae features in finger print images and the process of creating an OpenCV structure for minutiae extraction and matching of fingerprints.

Empowering artificial intelligence-based multi-biometric image sensor for human identification

Artificial intelligence (AI) and sensor technology developments have sparked revolutionary shifts in a number of fields, including biometric Identification. In order to improve human identification processes, this research offers a novel method that integrates AI and many biometric image sensors. The accuracy, robustness, and susceptibility to spoofing assaults of conventional single-modal biometric systems are among their many drawbacks. To overcome these challenges, we introduce a secure multi-biometric system that relies on feature-level fusion to identify users. In the preprocessing step, fingerprint images undergo Min-Max normalization to mitigate variations in image quality. In order to extract high-level features from both raw Electrocardiogram (ECG) signals and Min-Max normalized fingerprint images, ResNet50, a deep convolutional neural network, is used. These extracted feature vectors are able to distinguish between the two modalities. We proposed boosted Xgboost as a classifier for authentication in the identification steps to improve performance. The proposed approach is simulated using Python. A comparison study for improved Xgboost is presented using measures for accuracy, precision-recall, and F1-Score. Across all comparative metrics, the technique achieves much better performance. According to experimental findings, the suggested multi-biometric systems are more effective, dependable, and robust than the existing multi-biometric authentication systems.

D-π-A-π-D-Configured Imidazole-Tethered Benzothiadiazole-Based Sensor for the Ratiometric Discrimination of Picric Acid: Applications in Latent Fingerprint Imaging.

A tetraphenyl imidazole-appended benzothiadiazole-based fluorogenic probe (4,7-bis(4-(1,4,5-triphenyl-1H-imidazol-2-yl)phenyl)benzo[c][1,2,5]thiadiazole (BIPT)) has been successfully synthesized and characterized by NMR and high-resolution mass spectrometry (HRMS) spectral analyses. A low limit of detection (LOD) can be achieved to detect picric acid (PA; 7.89 nM). When benzothiadiazole acceptors are incorporated in the D-A-D probe, it can produce a large Stokes shift (206 nm) as a result. Fascinatingly, the fluorescence signals of BIPT were ratiometrically induced by the interaction with PA and exhibited an apparent emission shift from pink to green. The detection process of BIPT is triggered by an intermolecular charge transfer process, as the charge transfer takes place from the electron-rich imidazole to the electron-deficient PA. Moreover, fluorescence detection of PA has been employed in paper strips. Advantageously, sensor BIPT can potentially be applied to contact mode and real-time detection of PA in environmental water samples. Additionally, the BIPT sensor has been successfully employed for latent fingerprint imaging. The study provides clear insights into the rational design of chemosensors for sensing and real-time applications.

In situ coupling of carbon dots with zeolitic imidazolate frameworks enabling highly red emission in solid state

In this work, an extraordinary solid red emissive phosphor was prepared based on red-emitting carbon dots (R-CDs). The synthesis was conducted under an in-situ strategy, with assistance of zeolitic imidazolate frameworks. The obtained phosphor possesses a stronger red emission located at 630 nm in solid state, with CIE coordinate of (0.63, 0.35) and quantum yield of ∼ 45 %. As a consequence, not only aggregation-induced fluorescence quenching of R-CDs is avoided in solid state, but also an enhanced emission with high quantum yield is presented. Fluorescence properties were further explored in detail. The emission is found to be responsive to temperature and applied pressure. Based on the excellent emissive performance, the material has great potentials in anti-counterfeiting, latent fingerprint imaging, and temperature/pressure-sensing. This work provides a facile and promising way of preparing solid carbon-based phosphors for special applications.

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%.