Neural Network Architecture and Transient Evoked Otoacoustic Emission (TEOAE) Biometrics for Identification and Verification
Why this work is in the frame
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Bibliographic record
Abstract
This study presents a deep neural network architecture that achieves state of the art multi-session verification and identification performance for Transient Evoked Otoacoustic Emission (TEOAE) biometric system. TEOAE is a 20ms long response generated by the ear that is naturally strong against falsification, and replay attacks. It can be measured using a device with a speaker and multiple microphones. Previous TEAOE authentication methods focused on single-session or mixed-session performance. Our method focuses on multi-session authentication performance. We train a neural network model that generates a TEOAE embedding that is separable in Euclidean space by using the triplet loss function. These embeddings are used to create identity templates which are used to authenticate the user. We achieved identification accuracy of 99.3 ± 1.04%, and achieved an EER(Equal Error Rate) of 0.187 ± 0.146% for verification scenarios. Our method has achieved 7.56% performance increase for identification scenarios and 13.3% performance increase for verification scenarios over previous methods when averaged across all tests.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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