This paper investigates the feasibility of fusing two eye-centric authentication modalities—eye movements and periocular images—within a calibration-free authentication system. While each modality has independently shown promise for user authentication, their combination within a unified gaze-estimation pipeline has not been thoroughly explored at scale. In this report, we propose a multimodal authentication system and evaluate it using a large-scale in-house dataset comprising 9202 subjects with an eye tracking (ET) signal quality equivalent to a consumer-facing virtual reality (VR) device. Our results show that the multimodal approach consistently outperforms both unimodal systems across all scenarios, surpassing the FIDO benchmark. The integration of a state-of-the-art machine learning architecture contributed significantly to the overall authentication performance at scale, driven by the model’s ability to capture authentication representations and the complementary discriminative characteristics of the fused modalities.

Eye movement biometric has recently reached a meaningful performance threshold within a gaze estimation pipeline. Prior research claimed that good biometric performance can be achieved from a relatively large set of weakly intercorrelated features with high temporal persistence (indexed by the measurement of reliability). In this study, we revisit this hypothesis in the context of a modern deep learning (DL)-based eye movement biometric system, using a publicly available eye-movement dataset. Specifically, we investigate whether the measurement of reliability of learned embeddings continues to predict biometric performance in DL-based biometrics. Our results confirm that temporal persistence—quantified by measurement of reliability—is a significant predictor of performance in DL-based biometric systems, extending prior findings into the DL-based biometric. We also examine how manipulating eye-tracking signal quality descriptors impacts the temporal persistence of embeddings, finding that degradation of any kind undermines their temporal persistence. As a general matter, we found that measurement of reliability is an important predictor of DL-based biometric performance, and also that DL-learned embeddings are generally weakly intercorrelated.

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-(≈ 20 min) and long-term (≈ 1 year) biometric evaluations. Understanding the location of identity data within the eye movement spectrum is essential for privacy preservation.

Iris-based biometric authentication is a wide-spread biometric modality due to its accuracy, among other benefits. Improving the resistance of iris biometrics to spoofing attacks is an important research topic. Eye tracking and iris recognition devices have similar hardware that consists of a source of infra-red light and an image sensor. This similarity potentially enables eye tracking algorithms to run on iris-driven biometrics systems. The present work advances the state-of-the-art of detecting iris print attacks, wherein an imposter presents a printout of an authentic user’s iris to a biometrics system. The detection of iris print attacks is accomplished via analysis of the captured eye movement signal with a deep learning model. Results indicate better performance of the selected approach than the previous state-of-the-art.