Using deep autoencoders to learn robust domain-invariant representations for still-to-video face recognition
Why this work is in the frame
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Bibliographic record
Abstract
Video-based face recognition (FR) is a challenging task in real-world applications. In still-to-video FR, probe facial regions of interest (ROIs) are typically captured with lower-quality video cameras under unconstrained conditions, where facial appearances vary according to pose, illumination, scale, expression, etc. These video ROIs are typically compared against facial models designed with high-quality reference still ROI of each target individual enrolled to the system. In this paper, an efficient Canonical Face Representation CNN (CFR-CNN) is proposed for accurate still-to-video FR from a single sample per person, where still and video ROIs are captured in different conditions. Given a facial ROI captured under unconstrained video conditions, the CRF-CNN reconstructs it as a high-quality canonical ROI for matching that corresponds to the conditons of reference still ROIs (e.g., well-illuminated, sharp, frontal views with neutral expression). A deep autoencoder network is trained using a novel weighted loss function that can robustly generate similar face embeddings for the same subjects. Then, during operations, those face embeddings belonging to pairs of still and video ROIs from a target individual are accurately matched using a fully-connected classification network. Experimental results obtained with the COX Face and Chokepoint datasets indicate that the proposed CFR-CNN can achieve convincing level of accuracy. The computational complexity (number of operations, network parameters and layers) is significantly lower than state-of-the-art CNNs for video FR, and suggests that the CFR-CNN represents a cost-effective solution for real-time applications.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it