Benchmarking Microsaccade Recognition with Event Cameras: A Novel Dataset and Evaluation

TL;DR

This paper introduces a new event-based microsaccade dataset and evaluates spiking neural network models on microsaccade classification, demonstrating high accuracy and establishing a benchmark for future research in event-based vision.

Contribution

It presents the first high-fidelity event-based microsaccade dataset and a comprehensive evaluation of spiking neural networks for microsaccade recognition.

Findings

Models achieved around 90% accuracy in classifying microsaccades.

The dataset captures natural microsaccade dynamics with high temporal resolution.

Spiking neural networks are effective for fine motion recognition.

Abstract

Microsaccades are small, involuntary eye movements vital for visual perception and neural processing. Traditional microsaccade studies typically use eye trackers or frame-based analysis, which, while precise, are costly and limited in scalability and temporal resolution. Event-based sensing offers a high-speed, low-latency alternative by capturing fine-grained spatiotemporal changes efficiently. This work introduces a pioneering event-based microsaccade dataset to support research on small eye movement dynamics in cognitive computing. Using Blender, we render high-fidelity eye movement scenarios and simulate microsaccades with angular displacements from 0.5 to 2.0 degrees, divided into seven distinct classes. These are converted to event streams using v2e, preserving the natural temporal dynamics of microsaccades, with durations ranging from 0.25 ms to 2.25 ms. We evaluate the dataset using Spiking-VGG11, Spiking-VGG13, and Spiking-VGG16, and propose Spiking-VGG16Flow, an optical-flow-enhanced variant implemented in SpikingJelly. The models achieve around 90 percent average accuracy, successfully classifying microsaccades by angular displacement, independent of event count or duration. These results demonstrate the potential of spiking neural networks for fine motion recognition and establish a benchmark for event-based vision research. The dataset, code, and trained models will be publicly available at https://waseemshariff126.github.io/microsaccades/ .