Fingerprint Identification Research Articles

Prototype-Guided Autoencoder for OCT-Based Fingerprint Presentation Attack Detection

Anti-spoofing ability is vital for fingerprint identification systems. Conventional fingerprint scanning devices can only obtain information from the fingertip surfaces, and their performance is susceptible to skin conditions and presentation attacks (PAs). However, optical coherence tomography (OCT) can scan subcutaneous tissue and obtain 3D fingerprint structures, naturally enhancing its PA detection (PAD) ability from the perspective of hardware. Existing unsupervised PAD methods are based on image reconstruction. However, the reconstruction error is easily affected by OCT noise and the rich details of OCT images. Therefore we propose feature-based reconstruction to alleviate this problem, called the prototype-guided autoencoder. The model consists of a memory module and a denoising autoencoder without the requirement of PA fingerprints. As only bona fide fingerprints are available during the training phase, the memory module contains the prototype features of the bona fide fingerprints. During the inference phase, as the prototype memory module is frozen, the reconstructed representation of the bona fide input is close to the bona fide fingerprint features. Calculating the distance between the original features and the prototype reconstructed representation of the sample can achieve PAD. To obtain a better decision making boundary, we propose a representation consistency constraint, which reduces the bona fide representation reconstruction distance closer, so that it is easier to differentiate between fingerprints and PAs.

Toward Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification

Radio frequency fingerprint identification (RFFI) can classify wireless devices by analyzing the signal distortions caused by intrinsic hardware impairments. Recently, state-of-the-art neural networks have been adopted for RFFI. However, many neural networks, e.g., multilayer perceptron (MLP) and convolutional neural network (CNN), require fixed-size input data. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the RFFI performance in such scenarios is often unsatisfactory. In this paper, we analyze the reason why MLP- and CNN-based RFFI systems are constrained by the input size. To overcome this, we propose four neural networks that can process signals of variable lengths, namely flatten-free CNN, long short-term memory (LSTM) network, gated recurrent unit (GRU) network, and transformer. We adopt data augmentation during training which can significantly improve the model’s robustness to noise. We compare two augmentation schemes, namely offline and online augmentation. The results show the online one performs better. During the inference, a multi-packet inference approach is further leveraged to improve the classification accuracy in low SNR scenarios. We take LoRa as a case study and evaluate the system by classifying 10 commercial-off-the-shelf LoRa devices in various SNR conditions. The online augmentation can boost the low-SNR classification accuracy by up to 50% and the multi-packet inference approach can further increase the accuracy by over 20%.

Gender Classification Based on Fingerprint Using Wavelet and Multilayer Perceptron

Fingerprint-based gender classification is beneficial for speeding up the fingerprint identification of criminals, accident victims, and natural disaster victims that are difficult to be recognized based on their physical characteristics. The biggest obstacle to digitally classifying fingerprints is the image's poor quality. Some methods have been developed to improve image quality through various preprocessing, such as noise removal, background segmentation, thinning, and binarization. However, as these processes increase the classification time, some methods have been developed to classify fingerprints without preprocessing. One of them that has shown excellent success is CNN (Convolutional Neural Network). The method does not require preprocessing, but the computation time is very long and requires large amounts of training data. This study proposed a new method that did not need any preprocessing by using wavelet decomposition combined with the max-pooling process to generate features. Firstly, the fingerprint image was decomposed with a Haar wavelet of 4 levels, and each level was followed by a max-pooling process with a 2´2 filter. After that, the resulting feature was used as training data for the Multilayer Perceptron (MLP) network. In this study, the training data was a dataset from NIST (National Institute of Standart and Technology), with 750 fingerprints consisting of male and female fingerprints, each as many as 375. The method could produce a total accuracy of 80.1%.

Toward better AFIS practice and process in the forensic fingerprint environment

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

Deep Learning-Based RF Fingerprint Identification With Channel Effects Mitigation

Deep Learning-Based RF Fingerprint Identification With Channel Effects Mitigation

Fingerprint Identification for Physical & Logical Systems by Triangular-Method

Fingerprint Identification for Physical & Logical Systems by Triangular-Method

Cyfrowy ślad linii papilarnych

Based on the example of activities performed in the Forensic Laboratory of the Police Headquarters in Szczecin in the case of the Central Police Investigation Bureau, the article presents the issue related to the possibility of obtaining investigation material for fingerprint examination in the form of graphic files and video files secured from smartphones with images of fingers and hands. Due to the high quality of a photograph and a single frame from a video, it was possible to positively identify the person, which made it possible to largely substantiate the person’s involvement in the criminal act. The widespread access to smartphones that enable taking and sharing high-quality graphic files, as well as a huge number of photographs and videos shared on the Internet, with increasing frequency, in combination with simple tools for processing video frames and unsophisticated procedures for rescaling traces in AFIS, make a new form of obtaining material for fingerprint identification in the form of a digital fingerprint, and may become more significant.

FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement.

Latent fingerprint enhancement is an essential preprocessing step for latent fingerprint identification. Most latent fingerprint enhancement methods try to restore corrupted gray ridges/valleys. In this paper, we propose a new method that formulates latent fingerprint enhancement as a constrained fingerprint generation problem within a generative adversarial network (GAN) framework. We name the proposed network FingerGAN. It can enforce its generated fingerprint (i.e, enhanced latent fingerprint) indistinguishable from the corresponding ground truth instance in terms of the fingerprint skeleton map weighted by minutia locations and the orientation field regularized by the FOMFE model. Because minutia is the primary feature for fingerprint recognition and minutia can be retrieved directly from the fingerprint skeleton map, we offer a holistic framework that can perform latent fingerprint enhancement in the context of directly optimizing minutia information. This will help improve latent fingerprint identification performance significantly. Experimental results on two public latent fingerprint databases demonstrate that our method outperforms the state of the arts significantly. The codes will be available for non-commercial purposes from https://github.com/HubYZ/LatentEnhancement.

Fingerprint Recognition Using Deep Learning - A Review

There have been efforts to address the problems with fingerprint identification systems that require physical contact by creating contactless fingerprint identification systems. Numerous studies on various aspects of contactless fingerprint processing, including the use of deep learning in various algorithmic frameworks, classical image processing, and the machine-learning pipeline, have been published. It was demonstrated that the deep learning-based solutions were more accurate than the alternatives. This effort was driven by a desire to provide a thorough assessment of these successes and their identified limitations. This study examined three approaches to contactless fingerprint recognition: (i) methods for capturing images of the fingerprint, (ii) traditional preprocessing techniques for enhancing fingerprint images for recognition tasks, and (iii) deep learning. (i) taking a picture of your finger, and (ii) using conventional image processing to get the picture ready for recognition. In total, eight research papers were found to meet both the inclusion and exclusion criteria. Based on this review's findings, we discussed the potential benefits of deep learning methods for biometrics and the challenges that still need to be overcome before these methods can be used in practical biometric settings.

FUID SEVM Fingerprint Unique ID Smart Electronic Voting Machine

Abstract: Secure voting is ensured by the Electronic Voting Machine. People should believe that their vote is secured and there are no unfair practices. The main objective of this project is to develop a secure Electronic Voting Machine using fingerprint identification method, we are using system database for fingerprint access. At the time of voting in elections, the verification of the electronic voting process can be done by scanning the veins on the fingers which allows voting to be reset so voters can cast their vote. We are also giving a barcode scan feature where user will scan to authenticate himself. System then show the list of candidate user, User will vote by pressing the button and all the data will be store in storage device. Fingerprint scanning is used to provide security to prevent fake, duplicate voting etc. It also increases the accuracy and speed of the process. The purpose of such a system is to ensure that only the legitimate user and no one else has access to the voting rights. If a particular pattern matches anyone in the available record, voting access is granted. But in case the pattern does not match the records in the database, or in the case of repetition, access to cast a vote is denied or the vote is rejected.

Andy Warhol and His Amazing Technicolor Shoes: Characterizing the Synthetic Dyes Found in Dr. Ph. Martin’s Synchromatic Transparent Watercolors and Used in À la Recherche du Shoe Perdu

Synthetic organic dyes were extensively used by artists in the first half of the 20th century, knowingly or otherwise. This included Andy Warhol and his À la Recherche du Shoe Perdu (c. 1955), a major portfolio of hand-colored prints, a copy of which resides in the collection of The Museum of Modern Art (MoMA). Warhol and his friends were known to use Dr. Ph. Martin’s Synchromatic Transparent Water Colors to bring these prints to life. A historical set of Synchromatic Transparent Watercolors were initially investigated by UV-visible spectroscopy, and samples from the historic set were also characterized by µ-Fourier transform infrared spectroscopy for fingerprint identification. To better elucidate the nature of the mixtures present, thin-layer chromatography was coupled with surface-enhanced Raman spectroscopy to separate the components of all colorants in the set. The dyes decisively identified include Acid Red 73, Acid Red 87, Acid Red 17, Acid Red 103, Basic Red 1, Acid Orange 7, Acid Yellow 23, Acid Green 1, Basic Green 4, Acid Blue 3, Acid Blue 93, Basic Violet 3, Basic Violet 10, Basic Violet 17, and Acid Black 2. Overall, Acid Blue 3, along with Acid Orange 7 and Acid Black 2, were found in the greatest number of dyes in the Dr. Ph. Martin’s set. Data from the historic set was subsequently used for direct comparison with reflectance spectra from the Warhol portfolio using principal component analysis. Microfade testing on a Synchromatic Transparent Watercolors brochure was also conducted to identify fugitive colorants, the results of which were extrapolated to each of the prints in the Warhol portfolio. The analysis provided further insight into the dyes used in À la Recherche du Shoe Perdu and confirmed the extreme light sensitivity of some colorants and the fastness of others.

Image Fusion using Waveatom Transform for Medical Applications

Image fusion is the process of gathering all the important information of multiple images into a single fused image. It is used in many fields like medical, military, remote sensing etc... Image fusion in a medical field is extensively used by physicians for analysis and treatment, as the fused image contains complementary features present in different medical images obtained from imaging devices. The wave atom transform based medical image fusion is proposed as the potential capabilities of wave atoms have been exploded in many applications like image denoising, fingerprint identification, compression etc. Medical image techniques became important tool for medical diagnosis and treatment. Medical images of different modalities such as MRI, CT, PET, SPECT provide information in a limited domain. In medical resources, MRI and CT images contain structural information whereas PET and SPECT images contain functional Information. Thus, single modality does not provide complete and accurate information, both structural as well as functional information is required. So far, many medical image fusion methods have been developed. The easiest way to perform image fusion is to perform weighted average of input images pixel by pixel. But this method produces side effects such as artifacts & contrast reduction. The proposed method is experimented on various medical images and compared with recent state of fusion methods. Results prove that images obtained from proposed method have better clarity and enhanced information and are practically more helpful for better treatment and quick analysis.

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

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

Full-color Persistent Room-temperature Phosphorescence from Carbon Dot Composites Based on a Single Nonaromatic Carbon Source.

Herein, a series of carbon dot composites (CDC) with full-color and long-lived room-temperature phosphorescence (RTP) are prepared by a simple solid-phase one-step method from a single non-conjugated and non-aromatic carbon source. The RTP emission wavelength can be adjusted from 462 to 623 nm by changing the feeding ratio and reaction temperature. The luminescent lifetime and quantum yield of a green emissive CDC (AB-CDC-3) reach 1.1 s and 39%, respectively, because of the close interaction between carbon dots and inorganic matrix. Due to the existence of multiple luminescent centers, these CDC exhibit excitation wavelength-dependent RTP and a white emission when excited at a specific wavelength. A single-component afterglow luminescent diode based on AB-CDC-4 shows a high-quality white emission with CIE of (0.30, 0.33) and color-rendering index of 88. Based on the unique photophysical properties of the composites, they exhibit huge application potential in the field of multilevel anti-counterfeiting, fingerprint identification, and optoelectronic devices.

The confound of head position in within-session connectome fingerprinting in infants

Individuals differ in their functional connectome, which can be demonstrated using a “fingerprinting” analysis in which the connectome from an individual in one dataset is used to identify the same person from an independent dataset. Recently, the origin of these fingerprints has been studied by examining if they are present in infants. The results have varied considerably, with identification rates from 10 to 90%. When fingerprinting has been performed by splitting a single imaging session into two split-sessions (within session), identification rates were higher than when two full-sessions (between sessions) were compared. This study examined whether a methodological difference could account for this variation. It was hypothesized that the infant's exact head position in the head coil may affect the measured connectome, due to the gradual inhomogeneity of signal-to-noise in phased-array coils and the breadth of possible positions for a small infant head in a head coil. This study examined the impact of this using resting state functional MRI data from the Developing Human Connectome Project second release. Using functional timeseries, fingerprinting identification was high (84-91%) within a session while between sessions it was low (7%).Using N = 416 infants’ head positions, a map of the average signal-to-noise across the physical volume of the head coil was calculated and was used (independent group of 44 infants with two scan sessions) to demonstrate a significant relationship between head position in the head coil and functional connectivity. Using only the head positions (signal-to-noise values extrapolated from the group average map) of the independent group of 44 infants, high identification success was achieved across split-sessions (within session) but not full-sessions (between sessions). Using a model examining factors influencing the stability of the functional connectome, head position was seen as the strongest of the explanatory variables. We conclude within-session fingerprinting is affected by head position and future infant functional fingerprint analyses must use a different strategy or account for this impact.

Zn2SiO4: Mn2+, Yb3+ long afterglow materials prepared employing Zn-based coordination polymer as precursor: Properties, Mechanism and Application

An excellent green persistent luminescence (PersL) phosphor Zn2SiO4: Mn2+, Yb3+ was synthesized through a new method via reacting Zn-based coordination polymer particles with SiO2 submicrospheres. The structure properties including the phase purity, structural parameters and crystal structure were investigated in details. Morphological analysis revealed that Zn2SiO4: Mn2+, Yb3+ retained the morphology of SiO2 submicrospheres with diameter ∼200 nm. The PersL of Zn2SiO4: Mn2+ displayed a remarkably enhanced afterglow duration by codoping with Yb3+, in which the duration time prolonged from 5 min to 114 min (∼23 times) as compared with that of Zn2SiO4 host or singly Mn2+ doped. The depths of the traps responsible for PersL were estimated to be in the range of 0.696–1.112 eV, and an additional trap that are deemed to play a role for the improvement was discussed. The prepared phosphors were successfully used for the latent fingerprint visualization and exhibited high accuracy of fingerprint recognition. These findings not only provids a new synthetic approach for PersL materials with designated morphology and dimensions, but also indicates the product may find applications in advanced latent fingerprint identification, information storage and anticounterfeiting technology.

Adversarial attacks and active defense on deep learning based identification of GaN power amplifiers under physical perturbation

Deep learning (DL)-based radiofrequency (RF) fingerprinting identification has shown significantly growing importance in the wireless industry including 5G, IoT and Wireless Sensor Networks (WSN). However, there is little investigation on the robustness of the corresponding DL models against adversarial attacks and adversarial defense. This paper discusses the effects of four cutting-edge adversarial attacks on DL-based RF fingerprinting identification and provides a graphical analysis of RF feature perturbations following adversarial attacks. For the first time we also demonstrate the results of combined physical attack (i.e. tuning the drain currents of the power amplifiers) by tuning the physical features of the device hardware (i.e. drain current), and adversarial attacks on DL-based classifiers. Experimental results from 16 Gallium nitride (GaN) power amplifiers (PA) reveal that adversarial attacks can seriously deteriorate the accuracy of RF identification from about 100.00% to below 10.00% even with only 0.50% adversarial perturbation. In order to deal with the problem of decreased accuracy caused by attacks, we proposed an adversarial defense method based on feature-level interpretability (FIAT), which improved the accuracy of RF identification with interpretability analysis. The results compared with several common defense methods validate that the proposed FIAT can maintain an accuracy of 96.00% even when the adversarial attack perturbation is 5.00% and with physical attack. Moreover, the changes of data features in the process of adversarial attacks and defense are given, providing a new way for the interpretability of adversarial attacks and defense on RF identification tasks.

Radio Frequency Fingerprinting Exploiting Non-Linear Memory Effect

Radio frequency fingerprint (RFF) identification distinguishes wireless transmitters by exploiting their hardware imperfection that is inherent in typical radio frequency (RF) front ends. This can reduce the risks for the identities of legitimate devices being copied, or forged, which can also occur in conventional software-based identification systems. This paper analyzes the feasibility of device identification exploiting the unique non-linear memory effect of the transmitter RF chains consisting of matched pulse shaping filters and non-linear power amplifiers (PAs). This unique feature can be extracted from the received distorted constellation diagrams (CDs) with the help of image recognition-based classification algorithms. In order to validate the performance of the proposed RFF approach, experiments are carried out in cabled and over the air (OTA) scenarios. In the cabled experiment, the average classification accuracy among systems of 8 PAs (4 PAs of the same model and the other 4 of different models) is around 92% at signal to noise ratio (SNR) of 10 dB. For the OTA line-of-sight (LOS) scenario, the average classification accuracy is 90% at SNR of 10 dB; for the non-line-of-sight (NLOS) scenario, the average classification accuracy is 79% at SNR of 12 dB.

A Study on Structural and Textural Feature Extraction for Contactless Fingerprint Classification

Objectives: The Recent COVID 19 Pandemic has capped the efficacy of the mostly used existing touch-based fingerprint detection. Hence, the main objective of this study is to develop a lightweight, robust and efficient touchless fingerprint identification model. To cope up with touchless environment demands, an emphasis is made on improvement in local conditions, feature modalities as well as learning environment. Methods: Considering these objectives, the focus of this study is on two different methods for classification of contactless fingerprint. In the first method structural features like minutiae details are extracted followed by classification by SSIM. In the second method GLCM textural features were extracted followed by classification using Random Forest algorithm. In the proposed method, performance assessment is done by considering data samples of 1000 random users that are collected from different benchmark databases like Hong Kong Polytechnic University 3D-fngerprint images Database Version 2.0, Touchless Fingerprint Database of IIT Bombay, IIT Kanpur, IIT Jodhpur. Findings: Though, touchless fingerprint detection is considered as a viable alternative; yet, the real-time data complexities like non-linear textural patterns, dusts, non-uniform local conditions like illumination, contrast, orientation make it complex for realization. Moreover, the likelihood of ridge discontinuity and texture damages can majorly limit its efficacy. Novelty: The proposed model mainly focusses on reducing Equal Error Rate and improving the accuracy of contactless fingerprint classification by extracting textural features rather just sticking to conventional structural feature-minutiae. The Proposed method outperforms when compared with the existing state of the art methods by achieving an accuracy of 94.72%, precision of 98.84%, recall of 97.716%, FMeasure 0.9827 and a reduced EER of about 0.084. The key novelty of this approach was that it doesn’t require any surface 3D reconstruction, rather it employed different mathematical approaches to retrieve surface normal and minutiae information. Keywords: SSIM; GLCM; Contactless Fingerprint; Minutiae; EER; Confusion Matrix

Identification of metabolic fingerprints in severe obstructive sleep apnea using gas chromatography-Mass spectrometry.

Objective: Obstructive sleep apnea (OSA) is considered a major sleep-related breathing problem with an increasing prevalence rate. Retrospective studies have revealed the risk of various comorbidities associated with increased severity of OSA. This study aims to identify novel metabolic biomarkers associated with severe OSA. Methods: In total, 50 cases of OSA patients (49.74 ± 11.87years) and 30 controls (39.20 ± 3.29years) were included in the study. According to the polysomnography reports and questionnaire-based assessment, only patients with an apnea-hypopnea index (AHI >30 events/hour) exceeding the threshold representing severe OSA patients were considered for metabolite analysis. Plasma metabolites were analyzed using gas chromatography-mass spectrometry (GC-MS). Results: A total of 92 metabolites were identified in the OSA group compared with the control group after metabolic profiling. Metabolites and their correlated metabolic pathways were significantly altered in OSA patients with respect to controls. The fold-change analysis revealed markers of chronic kidney disease, cardiovascular risk, and oxidative stress-like indoxyl sulfate, 5-hydroxytryptamine, and 5-aminolevulenic acid, respectively, which were significantly upregulated in OSA patients. Conclusion: Identifying these metabolic signatures paves the way to monitor comorbid disease progression due to OSA. Results of this study suggest that blood plasma-based biomarkers may have the potential for disease management.