Face Recognition in Video Using a What-and-Where Fusion Neural Network
Bibliographic record
Abstract
A what-and-where fusion neural network is applied to the recognition of human faces from video sequences. The spatio-temporal information contained in successive video frames allows to effectively accumulate a classifier's predictions for each person being tracked in an environment. In a particular realization of this network, a fuzzy ARTMAP neural network is used to classify faces detected in each frame, while a bank of Kalman filters is used to track blobs that contain the extracted faces moving in the environment. Performance of the what-and-where fusion neural network is compared to that of the fuzzy ARTMAP and k-nearest-neighbor (k-NN) classifiers in terms of classification rate, convergence time and compression. In this paper, the impact on performance of setting different region of interest (ROI), of optimizing fuzzy ARTMAP parameters, and of selecting different training subset sizes, is assessed. Simulation results on real-world video sequences indicate that this network can achieve a classification rate that is significantly higher (by approximately 50% in some cases) than that of fuzzy ARTMAP alone, and than that of the k-NN.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".