A Facial Morphology-Guided Feature Selection Method For Spontaneous Expression Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Facial Expression Recognition (FER) is a crucial aspect in various domains, given its significance in understanding human emotions. However, designing efficient FER systems entails addressing challenges in feature extraction and selection. While previous studies have primarily focused on static feature selection methods, these approaches often struggle with spontaneous expressions due to the unique facial characteristics of each individual. To address this challenge, we implemented a Facial Morphology-Guided Feature Selection Method that combines texture features using Local Binary Pattern histograms and geometric features employing linear and eccentricity features. Subsequently, we employ Recursive Feature Elimination (RFE) and Binarized Genetic Algorithm (BGA) algorithms for feature selection, combining their outputs to identify the optimal subset of features tailored for each face. Experimental validation using the CK+ and DISFA datasets demonstrates the effectiveness of our approach in enhancing facial expression recognition accuracy.
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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 | 0.002 |
| Open science | 0.001 | 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