Signal vs Noise in Eye-tracking Data: Biometric Implications and Identity Information Across Frequencies

TL;DR

This study investigates how different frequency components of eye-tracking data contain biometric information, revealing that both signal and noise frequencies can be used for individual identification and have privacy implications.

Contribution

It demonstrates that not only the low-frequency 'signal' but also the high-frequency 'noise' in eye-tracking data carry significant biometric information, challenging previous assumptions.

Findings

Signal frequencies contain reliable biometric data.

Noise frequencies also possess individual identity information.

Biometric performance remains consistent over one year.

Abstract

Prior research states that frequencies below 75 Hz in eye-tracking data represent the primary eye movement termed ``signal'' while those above 75 Hz are deemed ``noise''. This study examines the biometric significance of this signal-noise distinction and its privacy implications. There are important individual differences in a person's eye movement, which lead to reliable biometric performance in the ``signal'' part. Despite minimal eye-movement information in the ``noise'' recordings, there might be significant individual differences. Our results confirm the ``signal'' predominantly contains identity-specific information, yet the ``noise'' also possesses unexpected identity-specific data. This consistency holds for both short-(approx. 20 min) and long-term (approx. 1 year) biometric evaluations. Understanding the location of identity data within the eye movement spectrum is essential for privacy preservation.