Event-based Liveness Detection using Temporal Ocular Dynamics: An Exploratory Approach

• URL: http://arxiv.org/abs/2604.26285v1
• Date: Wed, 29 Apr 2026 04:31:04 GMT
• Title: Event-based Liveness Detection using Temporal Ocular Dynamics: An Exploratory Approach
• Abstract summary: Event cameras capture sparse changes in brightness with microsecond resolution, enabling precise analysis of fast eye movements.<n>We design a data collection protocol to extend RGBE-Gaze with replay-attack recordings, yielding an event-based fake counterpart for liveness detection.<n>Our results show that event-based representations enable reliable discrimination between genuine and replayed sequences, achieving up to 95.37% top-1 accuracy with a spiking convolutional neural network.
• Score: 1.0874100424278175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face liveness detection has been extensively studied using RGB cameras, achieving strong performance under controlled conditions but often failing to generalize across sensors and attack scenarios. In this work, we explore event cameras as an alternative sensing modality for liveness detection based on temporal ocular dynamics. Event cameras capture sparse, asynchronous changes in brightness with microsecond resolution, enabling precise analysis of fast eye movements such as saccades. Replay attacks cannot faithfully reproduce these dynamics due to temporal resampling and display artifacts, leading to distinctive spatio-temporal patterns in the event domain. We design a data collection protocol to extend RGBE-Gaze with replay-attack recordings, yielding an event-based fake counterpart for liveness detection. We analyze event-driven temporal features from eye regions and evaluate their effectiveness for ocular motion segmentation and liveness classification. Our results show that event-based representations enable reliable discrimination between genuine and replayed sequences, achieving up to 95.37% top-1 accuracy with a spiking convolutional neural network. These preliminary findings highlight the potential of event-based sensing for robust and low-latency liveness detection.

Related papers

• EventGait: Towards Robust Gait Recognition with Event Streams [50.890621860023]
  Event cameras offer microsecond temporal resolution and high dynamic range, naturally capturing robust dynamic cues and suppressing static noise.<n>We propose textbfEventGait, an end-to-end dual-stream framework that separately models motion and shape while preserving the advantages of events.<n>Our approach sets a new state of the art on both synthesized and real-world event-based gait benchmarks, highlighting the robustness and potential of event-driven gait analysis.
  arXiv  Detail & Related papers  (2026-05-21T08:12:04Z)
• Towards Video Anomaly Detection from Event Streams: A Baseline and Benchmark Datasets [71.53287557600177]
  We take the first major step toward establishing event-based anomaly detection as a unified research direction.<n>We first construct multiple eventstream based benchmarks for video anomaly detection, featuring synchronized event and RGB recordings.<n>We then propose an EVent-centric Video Anomaly Detection framework, namely EWAD, with three key innovations.
  arXiv  Detail & Related papers  (2026-03-26T03:33:33Z)
• Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-Processing [2.5465367830324905]
  Event-based eye tracking holds significant promise for fine-grained cognitive state inference.<n>We introduce a model-agnostic, inference-time refinement framework to enhance the output of existing event-based gaze estimation models.
  arXiv  Detail & Related papers  (2025-06-14T14:48:11Z)
• SuperEIO: Self-Supervised Event Feature Learning for Event Inertial Odometry [6.552812892993662]
  Event cameras asynchronously output low-latency event streams, promising for state estimation in high-speed motion and challenging lighting conditions.<n>We propose SuperEIO, a novel framework that leverages the learning-based event-only detection and IMU measurements to achieve eventinertial odometry.<n>We evaluate our method extensively on multiple public datasets, demonstrating its superior accuracy and robustness compared to other state-of-the-art event-based methods.
  arXiv  Detail & Related papers  (2025-03-29T03:58:15Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Research, Applications and Prospects of Event-Based Pedestrian Detection: A Survey [10.494414329120909]
  Event-based cameras, inspired by the biological retina, have evolved into cutting-edge sensors distinguished by their minimal power requirements, negligible latency, superior temporal resolution, and expansive dynamic range. Event-based cameras address limitations by eschewing extraneous data transmissions and obviating motion blur in high-speed imaging scenarios. This paper offers an exhaustive review of research and applications particularly in the autonomous driving context.
  arXiv  Detail & Related papers  (2024-07-05T06:17:00Z)
• Heart Rate Detection Using an Event Camera [1.8020166013859684]
  Event cameras, also known as neuromorphic cameras, are an emerging technology that offer advantages over traditional shutter and frame-based cameras. We propose to harnesses the capabilities of event-based cameras to capture subtle changes in the surface of the skin caused by the pulsatile flow of blood in the wrist region.
  arXiv  Detail & Related papers  (2023-09-21T08:51:30Z)
• Event-based Simultaneous Localization and Mapping: A Comprehensive Survey [52.73728442921428]
  Review of event-based vSLAM algorithms that exploit the benefits of asynchronous and irregular event streams for localization and mapping tasks. Paper categorizes event-based vSLAM methods into four main categories: feature-based, direct, motion-compensation, and deep learning methods.
  arXiv  Detail & Related papers  (2023-04-19T16:21:14Z)
• Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation [79.02808071245634]
  Event-based cameras are bio-inspired sensors that capture brightness change of every pixel in an asynchronous manner. Event streams are divided into grids in the x-y-t coordinates for both positive and negative polarity, producing a set of pillars as 3D tensor representation. Long memory is encoded in the hidden state of adaptive convLSTMs while short memory is modeled by computing spatial-temporal correlation between event pillars.
  arXiv  Detail & Related papers  (2023-03-17T12:12:41Z)
• Learning Monocular Dense Depth from Events [53.078665310545745]
  Event cameras produce brightness changes in the form of a stream of asynchronous events instead of intensity frames. Recent learning-based approaches have been applied to event-based data, such as monocular depth prediction. We propose a recurrent architecture to solve this task and show significant improvement over standard feed-forward methods.
  arXiv  Detail & Related papers  (2020-10-16T12:36:23Z)

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.