Study provides long-term EMG data for multi-day biometric authentication.
problem Limited long-term EMG data for multi-day biometric authentication.
method Collected EMG data from 43 participants over three days.
result Multi-day biometric authentication results in low error rates.
Paper presents a biometric system using footfalls for person identification.
problem Natural movement and privacy concerns in biometric systems.
method Fog computing architecture with geophones and data compression.
result Achieved high accuracy (73-95%) in person identification.
Enhanced ECG biometric system improves authentication accuracy.
problem Traditional authentication methods have security issues.
method Developed an ECG-based authentication system using machine learning.
result Achieved up to 92% identification accuracy.
With increasing concerns about security, the need for highly secure physical biometrics-based authentication systems utilizing \emph{cancelable biometric} technologies is on the rise. Because the problem of cancelable template generation deals with the trade-off between template security and matching performance, many …
New algorithm approximates large matrices by sampling column blocks, reducing overhead.
problem Approximating large matrices using limited row or column sampling.
method Sampling predefined blocks of columns, providing guarantees for approximation quality.
result Effective algorithm for distributed matrix approximation, demonstrated with real-world biometric data.
Deep convolutional architecture identifies eye movements for biometric faster and more accurately.
problem Biometric identification of eye movements for authentication.
method Developed a deep convolutional architecture to process raw eye-tracking signals.
result Achieved a lower error rate by one order of magnitude and faster identification time by two orders of magnitude.
Deep learning improves gait biometric recognition accuracy.
problem Low accuracy in existing CSI-based gait identification systems.
method Developed an end-to-end deep CSI learning system using deep neural networks.
result Achieved a top-1 accuracy of 97.12% for a dataset of 30 people.
OpticNet predicts IOL optical properties from biometric data, outperforming current methods.
problem Precise prediction of IOL optical properties for cataract surgery.
method Unsupervised, domain-specific, physically motivated optical refraction network.
result OpticNet outperforms current methods in predicting IOL optical properties.
New system uses wearable bio-signals for easy authentication.
problem Security of private information on wearables is a concern.
method Context-dependent soft-biometric authentication using heart rate, gait, and breathing audio.
result Binary SVM with RBF kernel achieves high accuracy and low EER.
Proposes a new CNN approach for multimodal biometric identification.
problem Improving biometric identification accuracy across multiple modalities.
method Uses a bank of modality-specific CNNs, fuses their outputs, and optimizes the system.
result Significantly outperforms unimodal systems and demonstrates reduction in parameters.
Adversarial deep learning improves EEG-based person identification.
problem Exploiting temporally correlated structures and session variability in EEG data.
method Adversarial inference approach to learn session-invariant representations.
result Improvements in person identification robustness from longitudinal EEG data.
Deep learning gait recognition from smartphone data in unconstrained settings.
problem Gait recognition in unconstrained environments using smartphones.
method Hybrid deep neural network combining convolutional and recurrent neural networks for gait feature extraction.
result Achieves over 93% accuracy in person identification and authentication.
The study detects and classifies touch gestures with high accuracy.
problem Detecting and classifying touch gestures from touch screens.
method Supervised learning techniques using a capacitive sensor array to record touch and swipe gestures.
result Logistic Regression models achieved over 95% accuracy for all gesture types.
Pairwise quantile regression tackles similarity scoring in biometric systems.
problem Analyzing errors in similarity scoring for facial recognition.
method Established theoretical guarantees for pairwise quantile regression solutions, leveraging sharp concentration results for U-processes. result Proved generalization bounds and identified conditions for fast learning rates.
A new framework for mobile authentication using deep metric learning.
problem Challenges in mobile authentication using behavioral biometrics.
method Deep metric learning, private data protection, flexible training scheduling.
result 95% authentication accuracy on public datasets, robust against attacks.
CSI-Net learns WiFi signals for body characterization and pose recognition.
problem Unified learning of body characteristics and pose recognition.
method Unified Deep Neural Network (DNN) for WiFi signal representation and multi-task learning.
result CSI-Net solves biometrics estimation and person recognition.
New model for disability insurance reserving handles delays in claim information.
problem Disability insurance claims are affected by long delays and adjudication processes.
method Proposes a new individual reserving model for real-time claim evolution.
result Shows that new reserves can be calculated as modifications of classic reserves.
New models for insurance claims accounting for delays.
problem Delayed claims processing affects real-time policy development.
method Introducing transaction time models to describe multi-state life insurance.
result Explicit expressions for transaction time reserves derived.
Deep learning detects arrhythmia from RR-interval ECG data.
problem Diagnosing arrhythmia using ECG data.
method Convolutional neural network (CNN) on time-sliced RR-interval data.
result Compact system achieves accurate arrhythmia detection.
The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of current analysis rely on the extraction of features characterizing the activity of single brain regions, like power-spectrum estimates, thus neglecting possible temporal dependencie…
Study uses stacked hourglass networks to improve facial landmark detection for medical diagnosis.
problem Improving accuracy of facial landmark detection for medical diagnosis.
method Conducted a study on landmark localisation methods using stacked hourglass networks.
result State-of-the-art stacked hourglass architecture outperforms traditional methods.
Proposes deep multimodal fusion for biometric identification.
problem Improving biometric identification accuracy with multiple modalities.
method Joint optimization of multiple modality-specific CNNs at different feature abstraction levels.
result Significant improvement in multimodal person identification performance.
Gait patterns reveal emotions, offering a non-invasive method for automated recognition.
problem Automated emotion recognition from gait patterns.
method Data collection, preprocessing, and classification techniques.
result Gait patterns can indicate different emotion states, making them a promising source for emotion detection.
Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this study, we collect and analyze behavioral biometrics data from 200subjects, each using their personal Android mobile device for a period of at least 30…
Joint PLDA models two factors for better speaker verification.
problem Improving speaker verification accuracy with a nuisance condition.
method Generalizes PLDA to model two sample-dependent factors, modifies training procedure.
result Significant performance gains in multilingual speaker-verification tasks.
ECGID research focuses on rest but not exercise, this study evaluates both.
problem ECGID performance under exercise is insufficiently studied.
method Various learning methods applied to ECG dataset.
result Current ECGID methods fail under exercise conditions.
A comparative study of the application of Gaussian Mixture Model (GMM) and Radial Basis Function (RBF) in biometric recognition of voice has been carried out and presented. The application of machine learning techniques to biometric authentication and recognition problems has gained a widespread acceptance. In this res…
Paper shows how to trick machine learning-based authentication systems.
problem Ineffectiveness of machine learning in authentication systems against sophisticated attacks.
method Constructing a blind attack using query synthesis and Explainable-AI techniques.
result Demonstrated that machine learning models can be fooled with under 100 queries.
New attacks bypass authentication models using mouse dynamics data.
problem Adversarial attacks on behavioural mouse authentication.
method Generative approaches to create adversarial mouse trajectories.
result Imitation-based attacks often outperform surrogate-based attacks.
Paper defends iris recognition from adversarial examples using wavelet decomposition.
problem Adversarial examples threaten deep neural networks in biometric applications.
method Wavelet domain denoising of input examples to detect and mitigate adversarial attacks.
result Proposed defense strategies improve recognition accuracy against adversarial attacks.
Study explores how dataset breadth and depth affect Siamese Neural Network performance.
problem Impact of dataset breadth and depth on Siamese Neural Network performance.
method Experiments with three keystroke datasets varying breadth and depth factors.
result Increasing dataset breadth improves model performance, while depth's impact varies by dataset type.
Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, mos…
New CNN architecture detects face spoofing with deep local features.
problem Face recognition systems are vulnerable to face spoofing attacks.
method Two-step CNN architecture: first learns features from facial regions, then fine-tunes on whole images.
result Improves face spoofing detection performance and convergence speed.
Deep neural networks improve face matching across different domains for banking security.
problem Matching facial images from ID documents with selfies for secure transactions.
method A novel deep learning architecture using two CNNs for cross-domain face matching.
result Accuracy rates higher than 93% on the FaceBank dataset.
New methods compress models without real data, reducing accuracy loss.
problem Compression requires real data, which is often unavailable or sensitive.
method Synthetic data generation from trained models for calibration and fine-tuning.
result Best method shows negligible accuracy loss compared to original training set.
Privacy-preserving identification framework using sparse approximation and privacy amplification.
problem Protecting privacy in identification applications like biometrics and IoT.
method Sparse approximation with privacy amplification, not symmetric for data owner and user.
result Preserves privacy at low computational cost, storage, and communication burdens.
Automobile theft detection method uses owner driving data clustering.
problem Automobile theft detection challenges with limited real data.
method Clustering owner driving data using k-means algorithm, then detecting theft by comparing reconstructed data.
result 99% accuracy in detecting vehicle theft using only owner driving data.
New privacy method for eye tracking data reduces correlations and maintains accuracy.
problem Privacy concerns in eye tracking data from VR/AR glasses.
method Transform-coding based differential privacy mechanism for eye movement data.
result Significant reductions in sample correlations and query sensitivities, providing high privacy without loss in accuracy.
Privacy-preserving eye tracking framework using synthetic images.
problem Preserving sensitive personal information in eye tracking technology.
method Randomized encoding for privacy, Support Vector Regression model training on synthetic eye images.
result Framework achieves real-time gaze estimation with same accuracy as non-private version.
New method quantifies life insurance risk using Monte Carlo simulations.
problem Quantifying long-term risks in insurance portfolios over a one-year horizon.
method Least-squares Monte Carlo methods to quantify impact of new experience.
result Model can be used as an internal model under Solvency II or SST.
Bayesian approach models match and non-match score distributions over continuous covariates.
problem Complex evaluation of model performance over continuous covariates in biometric verification.
method Generative model of score distributions, mixture models, local basis functions, Bayesian inference.
result Accurate and effective method for studying model performance over continuous covariates.
The paper tackles robust control for insurance contracts under uncertain transition rates.
problem Maximizing utility in insurance contracts with uncertain transition rates.
method Novel robust utility maximization problem under bounded cumulative transition rate uncertainty, using worst-case scenario analysis.
result Existence and uniqueness of worst-case and best-case reserves for insurance contracts.
Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension reduction due to its profound theoretical foundation and success in practical applications. In respect of multi-view learning, however, it is limited by its capability of only handling data represented by two-view features, while i…
Study uses GMM-UBM and i-vectors to assess Parkinson's patients via speech, handwriting, and gait.
problem Assessing neurological state of Parkinson's disease patients using speech, handwriting, and gait signals.
method GMM-UBM and i-vectors applied to speech, handwriting, and gait signals.
result Different feature sets from each signal are crucial for assessing Parkinson's patients.
The partially observable hidden Markov model is an extension of the hidden Markov Model in which the hidden state is conditioned on an independent Markov chain. This structure is motivated by the presence of discrete metadata, such as an event type, that may partially reveal the hidden state but itself emanates from a …
The paper models stochastic interest rates for life insurance using phase-type distributions.
problem Modeling stochastic interest rates in life insurance with matrix approach.
method Integrates piecewise deterministic interest rates into a Markov jump process framework.
result Explicit formulas for reserves and future payments can be derived.
Dictionary learning algorithms have been successfully used for both reconstructive and discriminative tasks, where an input signal is represented with a sparse linear combination of dictionary atoms. While these methods are mostly developed for single-modality scenarios, recent studies have demonstrated the advantages …
The paper proposes using theoretical ROC curves to categorize classifier responses.
problem The lack of explicit probability distributions for classifier responses in machine learning.
method Fit beta distributions to classifier responses and use them to categorize responses into different classes.
result Established a categorization of classifier responses into classes with different ROC curve extremal behaviors.