Automated Lip Contour Detection Using the Level Set Segmentation Method
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Bibliographic record
Abstract
This paper describes a fully automated technique of detecting lip contours from static face images. Face detection is performed first on the input image using a variation of the AdaBoost classifier trained with Haar- like features extracted from the face. A second trained classifier is applied over this extracted face region for isolating the mouth section. The detection of lip contour is then performed from this isolated mouth region using the level set method of image segmentation. A new variational formulation of level set method, proposed by Li et al. (CVPR-2005), has been applied here that forces the level set function to be close to a signed distance function and therefore completely eliminates the need of the costly reinitialization procedure. The proposed method has been tested on three different face databases that contain images of both neutral faces as well as facial expressions, and a maximum successful lip contour detection rate of 91.08% has been achieved.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 it