The percentile-matching technique for synthetic eye tracking signal degradation: A biometric case study

This manuscript demonstrates an improved model-based approach for synthetic degradation of pre-recorded eye movement signals. Recordings from a high-quality eye tracking sensor are transformed to make their eye tracking signal quality resemble ones captured on a lower-quality target device. The proposed model improves the realism of the degraded signals versus prior approaches by introducing a mechanism for degrading spatial accuracy and temporal precision. Specifically, a percentile-matching technique is developed for mimicking the relative distributional structure of the target data signal quality characteristics. The model is demonstrated to improve realism on a per-feature and per-recording basis using data from EyeLink 1000 and SMI eye tracker embedded within a virtual reality platform. This study is first to show that the percentile-matching technique enables more accurate approximation of the target set using the biometric user authentication as an end task. Its mean absolute error of estimating the target set performance level is better for all three metrics considered (compared to the baseline) - a) 0.26 (versus 0.84) of d-prime; b) 12.08% (versus 31.10%) of False Rejection Rate at 1-in-10000 False Acceptance Rate; c) 3.08% (versus 4.69%) of Equal Error Rate. This paper also expands eye movement literature by suggesting an application-agnostic realism assessment workflow based on the 1 Nearest-Neighbor classifier. Our model improves the related median classification accuracy metric by 35.7% versus the benchmark model towards the ideal 50% value.