Multimodal User Authentication Method via Fusion of Keystroke Dynamics and Glove-Based Hand Kinematics

• URL: http://arxiv.org/abs/2607.24747v1
• Date: Tue, 19 May 2026 02:41:36 GMT
• Title: Multimodal User Authentication Method via Fusion of Keystroke Dynamics and Glove-Based Hand Kinematics
• Abstract summary: We propose a robust authentication framework that augments traditional keystroke dynamics using 19-dimensional hand kinematics.<n>Features are captured using a bespoke data glove equipped with 10 piezoresistive pressure sensors and a 9-axis IMU.<n>Averaged over five trials, the multimodal method achieved a mean Equal Error Rate (EER) of 2.12% for individual 600-ms authentication events.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although keystroke dynamics are cost-effective behavioral biometrics, their practical deployment is hindered by susceptibility to environmental variations. To address this, we propose a robust multimodal authentication framework that augments traditional keystroke dynamics using 19-dimensional hand kinematics. Features are captured using a bespoke data glove equipped with 10 piezoresistive pressure sensors and a 9-axis IMU. A hybrid CNN-LSTM architecture effectively fuses these heterogeneous time-series streams. To ensure real-world applicability, we implemented a rigorous "unseen" evaluation protocol: the model was trained on a desktop keyboard using data from 1 target user and 8 "known" impostors, but evaluated on a laptop keyboard (cross-domain) against the target and an "unknown" impostor excluded from training. Averaged over five trials, the multimodal method achieved a mean Equal Error Rate (EER) of 2.12% for individual 600-ms authentication events (Window 1). Crucially, aggregating scores over 10 events (6 seconds) using a temporal smoothing window yielded perfect authentication (0.00% EER). While our ablation study showed IMU kinematics alone achieved equivalent performance in a static laboratory, error analysis confirmed that pressure and IMU sensors, despite strong physical correlation, possess distinct sensitivities to error factors. Fusing these modalities establishes a vital fail-safe against real-world vulnerabilities like spatial spoofing and environmental noise, highlighting that combined physical traits provide much stronger biometric discrimination than timing features alone.

Related papers

• UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction [42.532335022354545]
  Anomaly detection in batch processes is hindered by transient dynamics, scarce fault labels, and reliance on singlemodality sensor data.<n>This work introduces UTOPYA, a multimodal framework that jointly addresses anomaly detection, time-series prediction, and phase classification in batch distillation.<n>A physics-informed regularisation scheme introduced in this work enforces temporal smoothness and thermodynamic monotonicity.
  arXiv  Detail & Related papers  (2026-05-18T10:28:15Z)
• QUACK! Making the (Rubber) Ducky Talk: A Systematic Study of Keystroke Dynamics for HID Injection Detection [60.105690843777666]
  We show that robust, privacy-preserving detection is achievable using lightweight models operating solely on timing features.<n>Our analysis reveals that attacker sophistication does not monotonically translate into improved evasion.
  arXiv  Detail & Related papers  (2026-04-17T08:53:24Z)
• Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions [59.49967357689445]
  Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies.<n>This study presents a complete open-source dilated real-based framework for the development and validation of deep learning models for affective touch recognition in soft interactive companions.
  arXiv  Detail & Related papers  (2026-04-16T10:20:05Z)
• PRISM: Performer RS-IMLE for Single-pass Multisensory Imitation Learning [51.24484551729328]
  We introduce PRISM, a single-pass policy based on a batch-global rejection-sampling variant of IMLE.<n> PRISM couples a temporal multisensory encoder with a linear-attention generator using a Performer architecture.<n>We demonstrate the efficacy of PRISM on a diverse real-world hardware suite, including loco-manipulation using a Unitree Go2 with a 7-DoF arm D1 and tabletop manipulation with a UR5 manipulator.
  arXiv  Detail & Related papers  (2026-02-02T17:57:37Z)
• Telling Human and Machine Handwriting Apart [7.418311309442571]
  Handwriting movements can be leveraged as a unique form of behavioral biometrics, to verify whether a real user is operating a device or application.<n>This task can be framed as a reverse Turing test in which a computer has to detect if an input instance has been generated by a human or artificially.<n>We train a shallow recurrent neural network that achieves excellent performance (98.3 percent Area Under the ROC Curve (AUC) score and 1.4 percent equal error rate on average across all synthesizers and datasets.
  arXiv  Detail & Related papers  (2026-01-16T18:45:16Z)
• Synheart Emotion: Privacy-Preserving On-Device Emotion Recognition from Biosignals [0.0]
  Most emotion recognition systems rely on cloud-based inference, introducing privacy vulnerabilities and latency constraints unsuitable for real-time applications.<n>This work presents a comprehensive evaluation of machine learning architectures for on-device emotion recognition from wrist-based photoplethysmography.<n>We deploy the wrist-only ExtraTrees model optimized via ONNX conversion, achieving a 4.08 MB footprint, 0.05 ms inference latency, and 152x speedup over the original implementation.
  arXiv  Detail & Related papers  (2025-11-09T05:15:04Z)
• Deep Learning Models for Robust Facial Liveness Detection [56.08694048252482]
  This study introduces a robust solution through novel deep learning models addressing the deficiencies in contemporary anti-spoofing techniques.<n>By innovatively integrating texture analysis and reflective properties associated with genuine human traits, our models distinguish authentic presence from replicas with remarkable precision.
  arXiv  Detail & Related papers  (2025-08-12T17:19:20Z)
• An Agent-Based Modeling Approach to Free-Text Keyboard Dynamics for Continuous Authentication [0.0]
  Continuous authentication systems leveraging free-text keyboard dynamics offer a promising additional layer of security in a multifactor authentication setup.<n>This study investigates the efficacy of behavioral biometrics by employing an Agent-Based Model (ABM) to simulate diverse typing profiles across mechanical and membrane keyboards.
  arXiv  Detail & Related papers  (2025-05-08T07:42:05Z)
• Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection [14.728689487990836]
  generative active adaptation framework minimizes labeling effort while enhancing model robustness.<n>We evaluate our end-to-end framework NetGuard on both simulated IDS data and a real-world ISP dataset.
  arXiv  Detail & Related papers  (2025-03-04T21:49:42Z)
• Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
  This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits. Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
  arXiv  Detail & Related papers  (2022-03-14T17:05:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.