Micro-Expression Recognition by Using CNN Features with PSO Algorithm and SVM Methods
Bibliographic record
Abstract
This study proposes a framework for defining ME expressions, in which preprocessing, feature extraction with deep learning, feature selection with an optimization algorithm, and classification methods are used. CASME-II, SMIC-HS, and SAMM, which are among the most used ME datasets in the literature, were combined to overcome the under-sampling problem caused by the datasets. In the preprocessing stage, onset, and apex frames in each video clip in datasets were detected, and optical flow images were obtained from the frames using the FarneBack method. The features of these obtained images were extracted by applying AlexNet, VGG16, MobilenetV2, EfficientNet, Squeezenet from CNN models. Then, combining the image features obtained from all CNN models. And then, the ones which are the most distinctive features were selected with the Particle Swarm Optimization (PSO) algorithm. The new feature set obtained was divided into classes positive, negative, and surprise using SVM. As a result, its success has been demonstrated with an accuracy rate of 0.8784 obtained in our proposed ME framework.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".