Estimating the Smile by Evaluating the Spread of Lips
Bibliographic record
Abstract
Smile is one of the important emotions that is essential in computer vision tasks. The greatly influenced part due to it is the lips. By encountering the changes in lips of smile images with respect to no smile, a smile detecting model can design for the computer vision tasks. In this paper, the approach is to evaluate the spread of lips. The lips movement distance is evaluated with respect to the eyes. 68 landmark points of dlib are used for this purpose. The left and right corners of lips are evaluated with the left and right eyes respectively using the count of landmark points (l and r). The secondary parameters - average, Maximum, and maxavgsum of l and r are used for evaluating the lip expansion variation. For each value of these parameters that can attain from l and r, the count of no smile images below it and count of smile images above it is considered and calculated the attainable efficiency. The value of secondary parameter having the maximum efficiency is defined as the threshold. The maximum efficiency that is attained due to average, Maximum and maxavgsum are 80.06, 67.3 and 78.54 respectively at the thresholds 2, 3 and 4.5 respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".