Keywords: eye tracking, biometrics, explainability, faithfulness, robustness
TL;DR: XAI feature attribution evaluation for eye movement based biometric models.
Abstract: Substantial advances in oculomotoric biometric identification have been made due to deep neural networks processing non-aggregated time series data that replace methods processing theoretically motivated engineered features. However, interpretability of deep neural networks is not trivial and needs to be thoroughly investigated for future eye tracking applications. Especially in medical or legal applications explanations can be required to be provided alongside predictions. In this work, we apply several attribution methods to a state of the art model for eye movement-based biometric identification. To asses the quality of the generated attributions, this work is focused on the quantitative evaluation of a range of established metrics. We find that Layer-wise Relevance Propagation generates the least complex attributions, while DeepLIFT attributions are the most faithful. Due to the absence of a correlation between attributions of these two methods we advocate to consider both methods for their potentially complementary attributions.
Submission Type: Full Paper
Supplementary Material: zip
Travel Award - Academic Status: Ph.D. Student
Travel Award - Institution And Country: University of Potsdam, Germany
Travel Award - Low To Lower-middle Income Countries: No, my institution does not qualify.
Camera Ready Latexfile: zip