Transparent non-intrusive multimodal biometric system for video conference using the fusion of face and ear recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mono-modal biometric systems face many limitations such as noisy data, intra-class variations, distinctiveness, spoof attacks, non-universality, and unacceptable error rates. Working on enhancing the performance of a mono-modal biometric system may not be highly efficient and effective. A multimodal biometric system combines two or more biometric features into a single identification system. It aims to improve several of the mono-modal biometric systems drawbacks and improve the recognition coverage and performance. In this paper, a transparent non-intrusive multimodal biometric system based on the fusion of the face and ear biometrics is proposed to identify individuals during a video conference environment with minimal explicit user involvement and hassle. The results of the experiment show that the performance of the transparent non-intrusive multimodal biometric system of the face and ear is higher than that of the mono-modal face or ear.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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