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.

We present an analysis of the eye tracking capabilities of the Meta Quest Pro virtual reality headset using a dataset of eye movement recordings collected from 78 participants. We highlight the potential differences in user experience as a function of device performance using a novel, user-centered evaluation framework for eye tracking signal quality analysis. In addition to presenting classical signal quality metrics such as spatial accuracy and spatial precision, we also explore how spatial accuracy varies across the field of view for different users across the performance range of the device. This work contributes to a growing understanding of eye tracking signal quality in virtual reality platforms, where the usability of gaze-based applications is directly dependent on the quality of the device’s eye tracking signal.

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.

This paper performs the crucial work of establishing a baseline for gaze-driven authentication performance to begin answering fundamental research questions using a very large dataset of gaze recordings from 9202 people with a level of eye tracking (ET) signal quality equivalent to modern consumer-facing virtual reality (VR) platforms. The size of the employed dataset is at least an order-of-magnitude larger than any other dataset from previous related work. Binocular estimates of the optical and visual axes of the eyes and a minimum duration for enrollment and verification are required for our model to achieve a false rejection rate (FRR) of below 3% at a false acceptance rate (FAR) of 1 in 50,000. In terms of identification accuracy which decreases with gallery size, we estimate that our model would fall below chance-level accuracy for gallery sizes of 148,000 or more. Our major findings indicate that gaze authentication can be as accurate as required by the FIDO standard when driven by a state-of-the-art machine learning architecture and a sufficiently large training dataset.

Thanks to the eye-tracking sensors that are embedded in emerging consumer devices like the Vive Pro Eye, we demonstrate that it is feasible to deliver user authentication via eye movement biometrics.

This paper proposes a novel evaluation framework, termed "critical evaluation periods," for evaluating continuous gaze prediction models. This framework emphasizes prediction performance when it is most critical for gaze prediction to be accurate relative to user perception. Based on perceptual characteristics of the human visual system such as saccadic suppression, this framework provides a more practical assessment of gaze prediction performance for gaze-contingent rendering compared to the dominant sample-by-sample evaluation strategy employed in literature, which overemphasizes performance during easy-to-predict periods of fixation. Using a case study with a lightweight deep learning gaze prediction model, we observe a significant discrepancy in the reported prediction accuracy between the proposed critical evaluation periods and the dominant evaluation strategy employed in literature. Based on our findings, we suggest that the proposed framework is more suitable for evaluating the performance of continuous gaze prediction models intended for gaze-contingent rendering applications.

Comparing the performance of new eye-tracking devices against an established benchmark is vital for identifying differences in the way eye movements are reported by each device. This paper introduces a new paired data set comprised of eye movement recordings captured simultaneously with both the EyeLink 1000—considered the “gold standard” in eye-tracking research studies—and the recently released AdHawk MindLink eye tracker. Our work presents a methodology for simultaneous data collection and a comparison of the resulting eye-tracking signal quality achieved by each device.

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.

Eye movement biometrics (EMB) is a relatively recent behavioral biometric modality that may have the potential to become the primary authentication method in virtual- and augmented-reality (VR/AR) devices due to their emerging use of eye-tracking sensors to enable foveated rendering techniques. However, existing EMB models have yet to demonstrate levels of performance that would be acceptable for real-world use. The present study proposes an improved methodology for EMB with the goal of satisfying the FIDO Biometrics Requirements’ recommendation of 5% false rejection rate at 1-in-10,000 false acceptance rate. A DenseNet-based convolutional neural network is proposed that is memory-efficient, relatively quick to train, and has only ~123K learnable parameters. The model is trained over an array of different eye-tracking tasks to improve the generalizability of learned features. Authentication performance is evaluated on a held-out set of up to 59 individuals across different eye-tracking tasks, test-retest intervals, and with increasing amounts of data available for enrollment and authentication. The impact of degraded sampling rates and spatial precision on authentication performance is also briefly explored to set the stage for future research targeting modern VR/AR devices. The proposed technique not only outperforms the previous state of the art but is also the first to approach a level of authentication performance that would be acceptable for real-world use.

Metric learning is a valuable technique for enabling the ongoing enrollment of new users within biometric systems. While this approach has been heavily employed for other biometric modalities such as facial recognition, applications to eye movements have only recently been explored. This manuscript further investigates the application of metric learning to eye movement biometrics. A set of three multilayer perceptron networks are trained for embedding feature vectors describing three classes of eye movements: fixations, saccades, and post-saccadic oscillations. The network is validated on a dataset containing eye movement traces of 269 subjects recorded during a reading task. The proposed algorithm is benchmarked against a previously introduced statistical biometric approach. While mean equal error rate (EER) was increased versus the benchmark method, the proposed technique demonstrated lower dispersion in EER across the four test folds considered herein.

As eye tracking can reduce the computational burden of virtual reality devices through a technique known as foveated rendering, we believe not only that eye tracking will be implemented in all virtual reality devices, but that eye tracking biometrics will become the standard method of authentication in virtual reality. Thus, we have created a real-time eye movement-driven authentication system for virtual reality devices. In this work, we describe the architecture of the system and provide a specific implementation that is done using the FOVE head-mounted display. We end with an exploration into future topics of research to spur thought and discussion.

This document describes a method for detecting the onset of eye fatigue and how it could be implemented in an existing live framework. The proposed method, which uses fixation data, does not rely as heavily on the sampling rate of the eye tracker as do methods which use saccade data, making it more suitable for lower cost eye trackers such as mobile and wearable devices. By being able to detect eye fatigue with such eye trackers, it becomes possible to react to the development of fatigue in virtually any environment, such as by alerting drivers that they appear fatigued and may want to pull over. It could also be used to aid in developing interfaces that are more user-friendly by noting at which point a user becomes fatigued while navigating the interface.