Individual‐specific management of reference data in adaptive ensembles for face re‐identification
Bibliographic record
Abstract
During video surveillance, face re‐identification allows recognition and targeting of individuals of interest from faces captured across a network of video cameras. In such applications, face recognition is challenging because faces are captured under limited spatial and temporal constraints. In addition, facial models for recognition are commonly designed using a limited number of representative reference samples from faces captured under specific conditions, regrouped into facial trajectories. Given new reference samples (provided by an operator or through some self‐updating process), updating facial models may allow maintenance of a high level of performance over time. Although adaptive ensembles have been successfully applied to robust modelling of an individual's facial appearance, reference data samples from a trajectory must be stored for validation. In this study, a memory management strategy based on Kullback–Leiber (KL) divergence is proposed to rank and select the most relevant validation samples over time in adaptive individual‐specific ensembles. When new reference samples become available for an individual, updates to the corresponding ensemble are validated using a mixture of new and previously‐stored samples. Only the samples with the highest KL divergence are preserved in memory for future adaptations. This strategy is compared with reference classifiers using videos from the face in action data. Simulation results show that the proposed strategy tends to select discriminative samples from wolf‐like individuals for validation. It allows maintenance of a high level of performance, while reducing the number of samples per individual by up to 80%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".