Automatic fiducial points detection for facial expressions using scale invariant feature
Bibliographic record
Abstract
Detecting fiducial points successfully in facial images or video sequences can play an important role in numerous facial image interpretation tasks such as face detection and identification, facial expression recognition, emotion recognition, and face image database management. In this paper we propose an automatic and robust method of facial fiducial point's detection for facial expressions analysis in video sequences using scale invariant feature based Adaboost classifiers. Face region is first located using the face detector with local normalization and optimal adaptive correlation technique. Candidate points are then selected over the face region using local scale-space extrema detection. The scale invariant feature for each candidate point is extracted for further examination. We choose 26 fiducial points on the face region from training samples to build the fiducial point detectors with Adaboost classifiers. All the candidate points in the test samples are examined through these detectors. Finally, all the 26 facial fiducial points are located on each frame of the test samples. Cohn-Kanade database and Mind Reading DVD are used for experiment. The results show that our method achieves a good performance of 90.69% average recognition rate.
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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".