Empirical Comparison of Face Verification Algorithms from UAVs
Why this work is in the frame
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Bibliographic record
Abstract
Face verification use cases have recently gained momentum in the increasingly digitalised society, and thus the need arises significantly to integrate this technology in wireless/mobile networked systems such as 5G and applications such as Unmanned Aerial Vehicle (UAV) based public safety services. However, there is no benchmarking result for the evaluation of the various existing face verification algorithms for such UAV applications. This paper is concerned with such new use cases (e.g., the Drone Guard Angel in the EU H2020 project ARCADIAN-IoT and the surveillance network applications in the EU H2020 project 5G-INDUCE), and provides an empirical comparison among three popular state-of-the-art face verification algorithms for this use case. To this end, a face verification pipeline is presented. These algorithms are then compared in terms of their inference time, and the distribution of the similarity indexes for different distances in UAV-based use cases. Their strengths and weaknesses are analysed, leading to an insightful recommendation on their applicability scenarios for UAVs.
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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 | 0.001 |
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