A hierarchical training and identification method using Gaussian process models for face recognition in videos
Bibliographic record
Abstract
In a video based face identification task, a sequence of frames can be utilized to identify the subject in the video. The information extracted from frames can provide samples of the subject in different head poses and facial expressions and under various lighting conditions which enriches the training process. However, some of these frames may not be useful for identification due to noise from various sources (such as occlusion, low resolution, and face tracking errors). It is important to reduce the effect of noisy samples by designing a representation structure that is capable of alleviating the noise in each sequence, complemented by developing a recognition procedure that rejects the wrong decisions affected by noise. In this paper we propose a sequence representation called Ensemble of Abstract Sequence Representatives (EASR) that is aimed at reducing the effect of noisy frames in a sequence. EASRs are used to guide the sampling process in a learning scheme called specialization - generalization which is used to train an ensemble of binary Gaussian Process (GP) models. Identification is done using: (i) the similarity between the EASRs of the gallery and probe images, and (ii) the label provided by the ensemble of GP classifier models. Evaluation of our approach on three publicly available benchmark datasets demonstrates significantly better performance compared to the state-of-the-art.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".