Facial expression recognition by applying multi-step integral projection and SVMs
Bibliographic record
Abstract
In order to achieve subject-independent facial feature detection and extraction and obtain robustness against illumination variety, a novel method of facial expression recognition using the combination of multi-step integral projection and Gabor transformation for feature detection and SVM for classification is presented in this paper. First, to avoid manually picked expression features, we propose a new approach called multi-step integral projection to detect and locate the exact position of human facial features automatically. Second, we segment the extracted areas into small cells for 7×7 pixels each and apply Gabor transformation on each cell. This greatly reduces the execution time of the Gabor transformation while retaining important information. Third, a Support Vector Machine is used for classifying facial emotions and we tested our system on the JAFFE database while achieving a high recognition rate of 94.8357% on trained data. Finally, we discuss the effect of different parameters selection in Gabor transformation and analyze the reason for some incorrect recognition.
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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".