Fingerprint Dataset Research Articles

Self-supervised learning for denoising of multidimensional MRI data.

To develop a fast denoising framework for high-dimensional MRI data based on a self-supervised learning scheme, which does not require ground truth clean image. Quantitative MRI faces limitations in SNR, because the variation of signal amplitude in a large set of images is the key mechanism for quantification. In addition, the complex non-linear signal models make the fitting process vulnerable to noise. To address these issues, we propose a fast deep-learning framework for denoising, which efficiently exploits the redundancy in multidimensional MRI data. A self-supervised model was designed to use only noisy images for training, bypassing the challenge of clean data paucity in clinical practice. For validation, we used two different datasets of simulated magnetization transfer contrast MR fingerprinting (MTC-MRF) dataset and in vivo DWI image dataset to show the generalizability. The proposed method drastically improved denoising performance in the presence of mild-to-severe noise regardless of noise distributions compared to previous methods of the BM3D, tMPPCA, and Patch2self. The improvements were even pronounced in the following quantification results from the denoised images. The proposed MD-S2S (Multidimensional-Self2Self) denoising technique could be further applied to various multi-dimensional MRI data and improve the quantification accuracy of tissue parameter maps.

Universal Fingerprint Generation: Controllable Diffusion Model with Multimodal Conditions.

The utilization of synthetic data for fingerprint recognition has garnered increased attention due to its potential to alleviate privacy concerns surrounding sensitive biometric data. However, current methods for generating fingerprints have limitations in creating impressions of the same finger with useful intra-class variations. To tackle this challenge, we present GenPrint, a framework to produce fingerprint images of various types while maintaining identity and offering humanly understandable control over different appearance factors such as fingerprint class, acquisition type, sensor device, and quality level. Unlike previous fingerprint generation approaches, GenPrint is not confined to replicating style characteristics from the training dataset alone: it enables the generation of novel styles from unseen devices without requiring additional fine-tuning. To accomplish these objectives, we developed GenPrint using latent diffusion models with multimodal conditions (text and image) for consistent generation of style and identity. Our experiments leverage a variety of publicly available datasets for training and evaluation. Results demonstrate the benefits of GenPrint in terms of identity preservation, explainable control, and universality of generated images. Importantly, the GenPrint-generated images yield comparable or even superior accuracy to models trained solely on real data and further enhances performance when augmenting the diversity of existing real fingerprint datasets.

CODI: Enhancing machine learning-based molecular profiling through contextual out-of-distribution integration

Abstract Molecular analytics increasingly utilize machine learning (ML) for predictive modeling based on data acquired through molecular profiling technologies. However, developing robust models that accurately capture physiological phenotypes is challenged by the dynamics inherent to biological systems, variability stemming from analytical procedures, and the resource-intensive nature of obtaining sufficiently representative datasets. Here, we propose and evaluate a new method: Contextual Out-of-Distribution Integration (CODI). Based on experimental observations, CODI generates synthetic data that integrate unrepresented sources of variation encountered in real-world applications into a given molecular fingerprint dataset. By augmenting a dataset with out-of-distribution variance, CODI enables an ML model to better generalize to samples beyond the seed training data, reducing the need for extensive experimental data collection. Using three independent longitudinal clinical studies and a case-control study, we demonstrate CODI's application to several classification tasks involving vibrational spectroscopy of human blood. We showcase our approach's ability to enable personalized fingerprinting for multi-year longitudinal molecular monitoring and enhance the robustness of trained ML models for improved disease detection. Our comparative analyses reveal that incorporating CODI into the classification workflow consistently leads to increased robustness against data variability and improved predictive accuracy.

Advanced Fingerprint Alteration Detection: A Comparative Analysis of Real and Synthetic Modifications Using InceptionV3 on the SOCOFing Dataset

This study investigates the efficacy of fingerprint alteration detection using Advanced Deep Learning techniques, specifically focusing on real and synthetically altered fingerprint images. Utilizing the Sokoto Coventry Fingerprint Dataset (SOCOFing), which comprises over 55,000 fingerprint images from 600 African subjects, we employed the Google InceptionV3 model to classify real and altered images under varying degrees of alteration. Our experimental results demonstrate a robust performance of the model, achieving an accuracy of 91.04% for detecting alterations with easy alteration parameter settings, 98.07% for medium alteration parameter settings, and 96.47% for hard alteration parameter settings. The findings highlight the potential of deep learning frameworks like InceptionV3 in enhancing the reliability of biometric systems by effectively distinguishing between real and altered fingerprints.

Recovery of Incomplete Fingerprints Based on Ridge Texture and Orientation Field

The recovery of mutilated fingerprints plays an important role in improving the accuracy of fingerprint recognition and the speed of identity retrieval, so it is crucial to recover mutilated fingerprints efficiently and accurately. In this paper, we propose a method for the restoration of mutilated fingerprints based on the ridge texture and orientation field. First, the part to be restored is identified via the local quality of the fingerprint, and a mask image is generated. Second, a novel dual-stream fingerprint restoration network named IFSR is designed, which contains two branches, namely an orientation prediction branch guided by the fingerprint orientation field and a detail restoration branch guided by the high-quality fingerprint texture image, through which the damaged region of the mutilated fingerprint is restored. Finally, the method proposed in this paper is validated on a real dataset and an artificially damaged fingerprint dataset. The equal error rate (EER) achieved on the DB1, DB2, and DB4 datasets of FVC2002 is 0.10%, 0.12%, and 0.20%, respectively, while on the DB1, DB2, and DB4 datasets of FVC2004, the EER reaches 1.13%, 2.00%, and 0.27%, respectively. On the artificially corrupted fingerprint dataset, the restoration method achieves a peak signal-to-noise ratio (PSNR) of 16.6735.

EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets

Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s location. Reducing the size of the RSS database enables faster processing, and saves storage space and radio resources necessary for the database transfer, thus cutting implementation and operation costs, and increasing the quality of service. In this work, we propose EWOk, an Element-Wise cOmpression using k-means, which reduces the size of the individual radio measurements within the fingerprinting radio map while sustaining or boosting the dataset’s positioning capabilities. We show that the 7-bit representation of measurements is sufficient in positioning scenarios, and reducing the data size further using EWOk results in higher compression and faster data transfer and processing. To eliminate the inherent uncertainty of k-means we propose a data-dependent, non-random initiation scheme to ensure stability and limit variance. We further combine EWOk with principal component analysis to show its applicability in combination with other methods, and to demonstrate the efficiency of the resulting multidimensional compression. We evaluate EWOk on 25 RSS fingerprinting datasets and show that it positively impacts compression efficiency, and positioning performance.

Enhanced fingerprint pattern classification: Integrating attention modules with lightweight deep learning models

AbstractLarge fingerprint databases can make the automated search process tedious and time‐consuming. Fingerprint pattern classification is a significant step in the identification system's complexity in terms of time and speed. Although several fingerprint algorithms have been developed for classification tasks, further improvements in performance and efficiency are still required. Most of the fingerprint algorithms use deep learning techniques. However, some deep learning techniques can be resource‐intensive and computationally expensive, while others can disregard the spatial relationships between the features used in classifying fingerprint patterns. This study proposes using lightweight deep learning models (i.e., MobileNet and EfficientNet‐B0) integrated with attention modules to classify fingerprint patterns. The two lightweight models are modified, yielding MobileNet+ and EfficientNet‐B0+ models. The lightweight deep learning models can help achieve optimal performance and reduce computational complexity. The attention modules focus on distinctive features for classification. Our proposed approach integrates four attention modules for fingerprint pattern classification into two lightweight deep learning models, that is, MobileNet+ and EfficientNet‐B0+. To evaluate our approach, we use two publicly available fingerprint datasets, that is, the NIST special database 301 dataset and the LivDet dataset. The evaluation results show that the EfficientNet‐B0+ model achieves the highest classification accuracy of 97% with only 854,086 training parameters. As a conclusion, we consider the training parameters small enough for the EfficientNet‐B0+ model to be deployed on low‐resource devices.

Blind-Touch: Homomorphic Encryption-Based Distributed Neural Network Inference for Privacy-Preserving Fingerprint Authentication

Fingerprint authentication is a popular security mechanism for smartphones and laptops. However, its adoption in web and cloud environments has been limited due to privacy concerns over storing and processing biometric data on servers. This paper introduces Blind-Touch, a novel machine learning-based fingerprint authentication system leveraging homomorphic encryption to address these privacy concerns. Homomorphic encryption allows computations on encrypted data without decrypting. Thus, Blind-Touch can keep fingerprint data encrypted on the server while performing machine learning operations. Blind-Touch combines three strategies to efficiently utilize homomorphic encryption in machine learning: (1) It optimizes the feature vector for a distributed architecture, processing the first fully connected layer (FC-16) in plaintext on the client side and the subsequent layer (FC-1) post-encryption on the server, thereby minimizing encrypted computations; (2) It employs a homomorphic encryption-compatible data compression technique capable of handling 8,192 authentication results concurrently; and (3) It utilizes a clustered server architecture to simultaneously process authentication results, thereby enhancing scalability with increasing user numbers. Blind-Touch achieves high accuracy on two benchmark fingerprint datasets, with a 93.6% F1- score for the PolyU dataset and a 98.2% F1-score for the SOKOTO dataset. Moreover, Blind-Touch can match a fingerprint among 5,000 in about 0.65 seconds. With its privacy-focused design, high accuracy, and efficiency, Blind-Touch is a promising alternative to conventional fingerprint authentication for web and cloud applications.

IoT Based Smart Attendance Management System

Abstract: This research work has application for attendance system of employer's and students in general. The system will facilitate institutions! organization to make attendance individual in time along with data information thumb impression will be taken as a signature for the system entry. Main design and challenge in this system is the design of database architecture and its business logic. I. AIMS AND OBJECTIVE The aim of this system is to implement in C#.net set of reliable techniques for fingerprint image enhancement and minutiae extraction. The performance of these techniques will be evaluated on a fingerprint data set. In combination with these development techniques, statistical experiments can then be performed on the fingerprint data set. The results from these experiments can be used to help us better understand what is involved in determining the statistical uniqueness of fingerprint minutiae. The main aim that this system would test whether attendance by fingerprint is enough for identification. It is expected that the work in this system will reach the stage of being able to fully test hypothesis. II. BACKGROUND/CONTEXT Fingerprints are the oldest form of biometric identification. Modem fingerprint-based identification is used in forensic science, and in biometric systems such as civilian identification devices. Keywords: the fingerprint data, statistical uniqueness of fingerprint minutiae.

NEURAL NETWORK-BASED ADVERSE DRUG REACTION PREDICTION USING MOLECULAR SUBSTRUCTURE ANALYSES

Objective: This study aims to enhance early detection and prediction by exploiting drug molecular substructures, overcoming challenges posed by limited authentic patient data in the medical domain. Methods: The study implemented a neural network approach to optimize molecular fingerprint algorithms and employed various machine learning algorithms for predictions. Additionally, the study identified and extracted substructures associated with severe Adverse Drug Reactions (ADRs), validating their presence within drug structures through a comparison with a random set of drug structures. Predictions were made for specific molecular structures, and results were validated using clinical evidence from the literature. Results: Optimized molecular fingerprint algorithms and diverse machine-learning models yielded promising outcomes. The Area Under Curve (AUC) value for the fingerprint dataset was obtained at approximately 65%, and integrating it with patient data significantly improved the performance by about 30%. Substructure analysis pinpointed key components linked to severe ADRs, reinforcing the predictive prowess of the model. Predictions for specific molecular structures were corroborated using clinical evidence from the literature, fortifying the credibility of the proposed approach. Conclusion: In conclusion, this research effectively tackles challenges in the early detection and prediction of ADRs by leveraging machine learning algorithms, focusing on drug molecular substructures. The optimized model, incorporating both fingerprint and patient datasets, demonstrated significant improvements in predictive performance. Identifying and validating substructures linked to severe ADRs contribute to the model's reliability. The study's findings are vital for advancing drug safety and laying the groundwork for further strides in predictive modeling within the medical domain.

Signed Fingermark Liveness Detection Method Based on Deep Residual Networks and Multimodal Decision Fusion

Fingermarks play an important role in document identification. At the same time, fingermarks on paper documents are often accompanied by signatures and background text, which introduce noise to the original fingermark textures and increase the difficulty of detection. A signed fingermark detection method based on deep residual networks and a decision-level fusion strategy was proposed to defend against spoofing attacks from fake fingermarks. Firstly, the multi-scale structure was introduced in the residual module, which improved the network’s depth and breadth without increasing the parameters. Then, the multi-probability label strategy was refined and employed to enhance the local encoding ability of the feature extraction. A score fusion strategy was designed, with weights allocated based on the difference in signed interference levels of local image blocks. Finally, a model fusion strategy based on evidence theory was suggested, which improved detection accuracy by leveraging complementarity between models. A large-scale fingermark database was established, which included real fingermarks made from real fingers and fake fingermarks made from various materials, and this was divided into two sub databases: signed and unsigned. The experimental results show that the proposed method achieves 96.16% accuracy based on the fingerprint dataset of the global liveness detection competition called LivDet2017 and achieves 99.30% accuracy based on the signed fingermark database, while it has good resistance to spoofing attacks from unknown materials.

Line image sensor-based colony fingerprinting system for rapid pathogenic bacteria identification

The rapid identification of pathogenic bacteria is crucial across various industries, including food or beverage manufacturing. Bacterial microcolony image-based classification has emerged as a promising approach to expedite identification, automate inspections, and reduce costs. However, conventional imaging methods have significant practical limitations, namely low throughput caused by the limited imaging range and slow imaging speed. To address these challenges, we developed an imaging system based on a line image sensor for rapid and wide-field imaging compared to existing colony imaging methods. This system can image a standard Petri dish (92 mm in diameter) completely within 22 s, successfully acquiring bacterial microcolony images. This process yielded a set of discrimination parameters termed as colony fingerprints, which were employed for machine learning. We demonstrated the performance of our system by identifying Staphylococcus aureus in food products using a machine learning model trained on a colony fingerprint dataset of 15 species from 9 genera, including foodborne pathogens. While conventional mass spectrometry-based methods require 24 h of incubation, our colony fingerprinting approach achieved 96% accuracy in just 10 h of incubation. Line image sensor offer high imaging speeds and scalability, allowing for swift and straightforward microbiological testing, eliminating the need for specialized expertise and overcoming the limitations of conventional methods. This innovation marks a transformative shift in industrial applications.

Impact of Finger Type in Contactless Fingerprint Verification

Contactless fingerprint authentication has gained popularity as a field of research in biometrics in recent years. Unlike traditional fingerprint recognition systems that require direct contact of the person’s finger with the sensor, contactless fingerprint systems offer several advantages, among them ease of capture and cost-effectiveness. Despite the progress made in this field, poor contrast, background noise, and limited image information continue to pose difficulties for fingerprint recognition in contactless environments. Furthermore, the number of images in published fingerprint biometric datasets for each person is restricted, and there is insufficient data for efficient training. Nevertheless, Convolutional Neural Network (CNN) architectures have been widely used, necessitating large databases. To address these issues, this paper introduces a Siamese network designed for the purpose of identity Verification, using the contactless thumb fingerprint modality to enhance recognition results. The Siamese network is able to extract pertinent features from noisy images with low contrast and limited information, even if they have limited information and low contrast. Additionally, this work proposes the use of the contactless thumb fingerprint modality instead of the contactless index fingerprint modality, which is more commonly used in related works. Consequently, the Mobile FingerPrint (MFP) dataset is introduced and constructed for evaluation. Experimental results demonstrate the efficiency of the proposed method, achieving an accuracy of 98.68% for thumb fingerprint Verification.

Impact of the rearing environment on the metabolism of shrimps and tracing the origins and species of shrimps using specific metabolites.

Herein, the link between rearing environmental condition and metabolism was explored. Metabolite fingerprint datasets of black tiger shrimp (Penaeus monodon) from three production sites were collected and studied using matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and HPLC-MS/MS. Two compounds, benzisothiazolinone and hippuric acid, were identified to be potentially related to pollution in the rearing environment and showed different abundances in the analysed shrimp samples with different origins. Furthermore, metabolomic analysis on three shrimp species, black tiger shrimp, kuruma shrimp (Penaeus japonicus) and sword shrimp (Parapenaeopsis hardwickii), under an identical rearing environment was also conducted. Two compounds, diethanolamine and benzisothiazolinone, potentially linked with pollution in the rearing environment were identified. The present protocol holds promise to be extended to the studies of exploring the relationship between rearing environmental conditions and metabolism. Furthermore, the analysis of single-blind samples was conducted. The results show that specific metabolites can be utilized as markers for tracing the origins of shrimp samples. The present protocol holds potential for application in tracing the origin and species of certain seafoods.

Automatic Fingerprint Data Labeling Using WiFi Signal and Smartphone Camera for Indoor Positioning

WiFi fingerprinting has been one of the most practical approaches for implementing an indoor positioning system. However, the need to measure location labels for fingerprint data has hindered the deployment of WiFi fingerprint‐based positioning systems. To reduce the cost of location labeling, several methods for automatically labeling locations or bypassing location labeling have been proposed. However, these methods still need to measure location labels for partial fingerprint data, or the estimated location labels are inaccurate. In this paper, we propose Heimdall, an automatic fingerprint data labeling method using a smartphone camera and WiFi signal for an indoor positioning system. Since Heimdall utilizes camera geometry for accurate automatic labeling, it aims to find the location labels of unlabeled fingerprint data composed of WiFi received signal strengths (RSSs) and photos collected using smartphones in multiple reference points (RPs). Using structure from motion (SfM) with all photos in the unlabeled fingerprint dataset, the location and orientation of the cameras can be recovered on an arbitrary world coordinate system. However, the world coordinate system derived by SfM is warped. In other words, it is different from the real‐world coordinate system where we express the location labels since SfM has similarity ambiguity, including scale, rotation, and translation ambiguity. Heimdall finds the location labels by estimating and applying an unwarping transformation that removes those ambiguities. We use two types of geometric constraints for estimating the unwarping transformation. First, several geometric entities in the warped‐world coordinates and real‐world coordinates are extracted from the photos and prior information about the indoor layout. The unwarping transformation transforms the geometric entities in warped coordinates to those in real‐world coordinates. Second, we utilize the log‐distance pathloss model with RSSs to derive the constraint about distances from the location label to the access points. We construct the optimization problem subject to those constraints to estimate the unwarping transformation. To evaluate the performance of Heimdall, we performed several experiments. Heimdall yields a mean location labeling error of 0.12 m and a standard deviation of location labeling error of 0.06 m for the best case in the experiment. These results demonstrate that a fingerprint‐based indoor positioning system can use the location labels estimated by Heimdall without performance degradation.

A Review of Open Access WiFi Fingerprinting Datasets for Indoor Positioning

A Review of Open Access WiFi Fingerprinting Datasets for Indoor Positioning

An Interpretable Siamese Attention Res‐CNN for Fingerprint Spoofing Detection

In recent years, fingerprint authentication has gained widespread adoption in diverse identification systems, including smartphones, wearable devices, and attendance machines, etc. Nonetheless, these systems are vulnerable to spoofing attacks from suspicious fingerprints, posing significant risks to privacy. Consequently, a fingerprint presentation attack detection (PAD) strategy is proposed to ensure the security of these systems. Most of the previous work concentrated on how to build a deep learning framework to improve the PAD performance by augmenting fingerprint samples, and little attention has been paid to the fundamental difference between live and fake fingerprints to optimize feature extractors. This paper proposes a new fingerprint liveness detection method based on Siamese attention residual convolutional neural network (Res‐CNN) that offers an interpretative perspective to this challenge. To leverage the variance in ridge continuity features (RCFs) between live and fake fingerprints, a Gabor filter is utilized to enhance the texture details of the fingerprint ridges, followed by the construction of an attention Res‐CNN model to extract RCF between the live and fake fingerprints. The model mitigates the performance deterioration caused by gradient disappearance. Furthermore, to highlight the difference in RCF, a Siamese attention residual network is devised, and the ridge continuity amplification loss function is designed to optimize the training process. Ultimately, the RCF parameters are transferred to the model, and transfer learning is utilized to aid its acquisition, thereby assuring the model’s interpretability. The experimental outcomes conducted on three publicly accessible fingerprint datasets demonstrate the superiority of the proposed method, exhibiting remarkable performance in both true detection rate and average classification error rate. Moreover, our method exhibits remarkable capabilities in PAD tasks, including cross‐material experiments and cross‐sensor experiments. Additionally, we leverage Gradient‐weighted Class Activation Mapping to generate a heatmap that visualizes the interpretability of our model, offering a compelling visual validation.

Wi-Fi Fingerprint for Indoor Keyless Entry Systems with Ensemble Learning Regression-Classification Model

Keyless entry systems have made human life more comfortable because the possibility of someone leaving a key is non-existent. However, there is a research opportunity to use Wi-Fi fingerprint localization in an indoor keyless entry system. The use of Wi-Fi as a solution is low cost because of the already hdeployed Wi-Fi access points (AP) around buildings. This study uses ensemble learning to utilize Wi-Fi fingerprints for keyless entry systems in indoor environments. The first step of this research is to obtain the UJIIndoorLoc Data Set, an open-source Wi-Fi fingerprint dataset. The next step is to design and implement a keyless entry system based on Wi-Fi Fingerprint. We compared four ensemble learning methods: Random Forest, AdaBoost, gradient boosting, and XGBoost. We use several metrics to compare the four methods, namely r2, the area under the receiver operating curve (AUROC), and the equal error rate (EER). The test results show that for localization based on ensemble regression, XGBoost has the best performance compared to the other three ensemble methods for both longitude and latitude predictions. The r2values are 0.94 and 0.98, respectively. For authentication based on ensemble classification, the gradient-boosting model performance increases as the number of weak learners increases. The optimum number of weak learners is 60. With AUROC = 0.99, gradient boosting outperforms random forest, AdaBoost, and XGBoost on authentication. Finally, our authentication method has EER = 0.01, which outperforms other state-of-the-art keyless entry systems. This research focuses on local building map datasets for future work.

Wi-Fi Fingerprint for Indoor Keyless Entry Systems with Ensemble Learning Regression-Classification Model

Keyless entry systems have made human life more comfortable because the possibility of someone leaving a key is non-existent. However, there is a research opportunity to use Wi-Fi fingerprint localization in an indoor keyless entry system. The use of Wi-Fi as a solution is low cost because of the already hdeployed Wi-Fi access points (AP) around buildings. This study uses ensemble learning to utilize Wi-Fi fingerprints for keyless entry systems in indoor environments. The first step of this research is to obtain the UJIIndoorLoc Data Set, an open-source Wi-Fi fingerprint dataset. The next step is to design and implement a keyless entry system based on Wi-Fi Fingerprint. We compared four ensemble learning methods: Random Forest, AdaBoost, gradient boosting, and XGBoost. We use several metrics to compare the four methods, namely r2, the area under the receiver operating curve (AUROC), and the equal error rate (EER). The test results show that for localization based on ensemble regression, XGBoost has the best performance compared to the other three ensemble methods for both longitude and latitude predictions. The r2values are 0.94 and 0.98, respectively. For authentication based on ensemble classification, the gradient-boosting model performance increases as the number of weak learners increases. The optimum number of weak learners is 60. With AUROC = 0.99, gradient boosting outperforms random forest, AdaBoost, and XGBoost on authentication. Finally, our authentication method has EER = 0.01, which outperforms other state-of-the-art keyless entry systems. This research focuses on local building map datasets for future work.

Quality and Feature Analysis of Parent and Child Fingerprints in West Bengal, India

Abstract: Fingerprint image of father, mother and child in ink paper method are collected from the region of West Bengal, India. One Hundred family without having kinship relation are involved in the data collection process. The paper is scanned with a HP scanner with 1200 ppi resolution and pre-processed with different technique to form a dataset of 1500 fingerprint images. Different non reference and reference image quality assessment technique are applied to test the quality of the image dataset. Minutiae based feature are extracted from the images to find a correlation between child and parent’s fingerprints. This family fingerprint dataset can be used by the researchers to find an alternative of DNA matching for parental verification or other research works.