STeW: Real-time Video Facial Emotion Classification via a Compact Sliding Temporal Windowed Deep Neural Network
Bibliographic record
Abstract
The real-time classification of human facial expressions presents achallenging task, even for humans. Individuals with Autism Spectrum Disorder (ASD) have an even greater difficulty in detectingand interpreting these facial expressions, which can lead to anincreased risk of depression and loneliness due to a disconnectwith society. This study explores a compact Sliding Temporal Windowed (STeW) deep neural network architecture for real-time videofacial emotion classification. The proposed STeW architecture isdesigned to provide a balance between speed and the leveragingof temporal characteristics to capture transient nuances of facialexpressions. A more difficult dataset (which we call BigFaceX) isproposed by combining and modifying the extended Cohn-Kanade(CK+), BAUM-1, and the eNTERFACE public datasets, and used toevaluate the proposed STeW network. Experimental results showthat the proposed STeW network architecture can achieve noticeably higher accuracy when compared to the highly compact mini-Xception network, thus illustrating the potential for leveraging thisapproach to achieve real-time video facial emotion classification.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".