Facial expression detection using Viola-Jones algorithm in the learning environment
Bibliographic record
Abstract
Abstract Emotion recognition using facial expression is an active research topic in the field of computer vision. In this paper, our system is based on a three-step approach, namely face detection, feature extraction and classification. Face detection takes photos/videos for information and finds face areas in these images. Facial extraction finds important highlights positions (eyes, mouth, nose and ocular temples) within a distinguished face using the Viola-Jones algorithm. In order to extract the faces, we have built a database of face images. We propose two systems: our first facial emotion recognition system supports the classification of the raw face inputs, the second extracts the Histogram of Oriented Gradient (HOG) from the face image. We use Support Vector Machines (SVM) for the classification phase. The experiments are conducted at the Ryerson Multimedia Laboratory (RML) dataset.The results of our experiments showed good accuracy compared to previous studies.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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".