Performance Evaluation of Machine Learning for Recognizing Human Facial Emotions
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
Facial Expression Recognition is a human emotion classification problem that attracted much attention from scientific research. Classifying human emotions can be a challenging task for machines. However, more accurate results and less execution time are there still the main issues when extracting features of human emotions. To cope with these challenges, we propose an automatic system that provides users with well-adopted classifier for recognizing facial expressions more accurately. The system consists of two fundamental machine-learning stages, namely, feature selection and feature classification. Feature selection is performed using Active Shape Model (ASM) composed of landmarks while the feature classification has examined seven well-known classifiers. We have used CK+ dataset, implemented and tested seven classifiers to find the best classifier. Experimental results showed that Quadratic classifier provides excellent performance and outperforms other classifiers with the highest accuracy of 92.42% on the same dataset.
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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 | 0.001 | 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 it