Robust Video Face Recognition From a Single Still Using a Synthetic Plus Variational Model
Bibliographic record
Abstract
Sparse representation-based classification (SRC) techniques have been shown to achieve a high level of performance in video-based face recognition (FR). However, matching faces captured in uncontrolled video conditions against a gallery with a single reference facial still per individual typically yields low accuracy. To improve robustness to intra-class variations, SRC techniques for FR have recently been extended to incorporate variational information from an external generic set into an auxiliary variational dictionary. Despite their success in handling linear variations, probe facial images with non-linear variations due to e.g., changes in pose and expressions, cannot be accurately reconstructed with a linear combination of images from gallery and auxiliary dictionaries because they do not share the same type of variations. In this paper, a new synthetic plus variational model is proposed to account for the non-linearities, particularly with pose variations. It reconstructs a probe image using (1) an auxiliary variational dictionary and (2) an augmented gallery dictionary enriched with a set of synthetic images generated from the reference faces with a wide diversity of pose angles. By solving a newly formulated simultaneous sparsity-based optimization problem, the augmented gallery dictionary is encouraged to share the same sparsity pattern with the variational dictionary for the same pose angles. In this way, each synthetic face in the augmented dictionary is combined with similar facial viewpoint in the variational dictionary. Experimental results obtained on Chokepoint and COX-S2V datasets, using different face representations, indicate that the proposed approach can outperform state-of-the-art SRC-based methods for still-to-video FR with a SSPP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".