Dynamic Facial Expression Recognition Using Fuzzy Hidden Markov Models
Bibliographic record
Abstract
Humans are immersed in a high-tech computing environment. Dependence on the pervasiveness of modern devices encompasses even simple everyday tasks. Unfortunately, the rapidly increasing expectations on the intelligence of these devices often exceeds their abilities. Devices are expected to perceive their environment, understand our intent, and autonomously carry out appropriate tasks. This paper presents an approach to bring device perception one step closer to meeting the high user expectations. A novel application of fuzzy hidden Markov models to automatically identify dynamic facial expressions is proposed in a human-device interaction context. A low-complexity vision based facial feature tracker is integrated with a hidden Markov model adapted to take advantage of fuzzy measures. Benefits over traditional hidden Markov mode Is for facial expression recognition include a reduction in training time, and improved flexibility for use as a valuable part of a larger multimodal system for natural human-device interaction
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".