EyeDrive: A Deep Learning Model for Continuous Driver Authentication
Why this work is in the frame
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Bibliographic record
Abstract
Eye movement (EM) is considerably a new behavioral modality for biometric authentication. In this work, we use this modality in the context of continuous driver authentication. Existing models rely on different modalities that limit their usage or are inconvenient to drivers. We propose an end-to-end learning model that takes the remote eye movement profiles solely and produces embeddings for driver authentication scenarios. The model is based on Long short-term memory (LSTM) and dense networks to learn temporal characteristics from the EM profiles. We focus on low-rate devices because of their affordability. Yet, they present a challenge because of their limited ability to capture quality measurements. To evaluate our model, two low frame-rate devices are used to build our datasets, which are Autocruis and GazePoint. The authentication performance outperforms state-of-the-art with as low as 30 seconds frame length with both devices. The best authentication performances for the identification/verification modes are 92.38/0.76% and 91.05/0.11% for the first and the second datasets, respectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it