Emotion recognition using dynamic grid-based HoG features
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automatic facial expression analysis is the most commonly studied aspect of behavior understanding and human-computer interface. The main difficulty with facial emotion recognition system is to implement general expression models. The same facial expression may vary differently across humans; this can be true even for the same person when the expression is displayed in different contexts. These factors present a significant challenge for the recognition task. The method we applied, which is reminiscent of the “baseline method”, utilizes dynamic dense appearance descriptors and statistical machine learning techniques. Histograms of oriented gradients (HoG) are used to extract the appearance features by accumulating the gradient magnitudes for a set of orientations in 1-D histograms defined over a size-adaptive dense grid, and Support Vector Machines with Radial Basis Function kernels are the base learners of emotions. The overall classification performance of the emotion detection reached 70% which is better than the 56% accuracy achieved by the “baseline method” presented by the challenge organizers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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 it