Incremental update of biometric models in face-based video surveillance
Bibliographic record
Abstract
Video-based face recognition of individuals involves matching facial regions captured in video sequences against the model of individuals enrolled to a face recognition system. Due to a limited control over operational conditions, classification systems applied to face matching are confronted with complex pattern recognition environments that change over time. Therefore, the facial model of an individual tends to diverge from the underlying data distribution. Although a limited amount of reference data is often collected during initial enrollment, new samples often become available over time to update and refine models. In this paper, an adaptive ensemble of classifiers is proposed to update facial models in response to new reference samples. To avoid knowledge corruption linked to incremental learning of monolithic classifiers, and maintain a high level of performance, this ensemble exploits a learn-and-combine approach. In response to new reference samples, a new 2-class Probabilistic Fuzzy ARTMAP classifier is trained and combined to previously-trained classifiers in the ROC space. Iterative Boolean Combination is employed for fusion of 2-class classifiers of each individual in the decision space. Performance is assessed in terms of AUC accuracy and resource requirements under different incremental learning scenarios with new data extracted from the Faces in Action data set. Simulation results indicate that the proposed system significantly outperforms reference classifiers and ensembles for incremental learning.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".