A CNN Approach to Micro-Expressions Detection
Bibliographic record
Abstract
Machine Learning and Convolutional Neural Networks (CNN) have significantly increased the performance in image recognition and are being widely adopted to analyze faces based on availability of very large databases of Figure and postures. A hot research interest of the the Face Recognition community is the recognition of different types of facial expressions. Among these, Facial Micro-Expressions (ME's) are of big interest due to subtle movements which can show deep or suppressed emotions of an individual. These micro-expressions are quite prominently being used in security, psychotherapy, neuroscience and other related disciplines. The major challenge encountered while detecting these expressions are their low intensity and short duration. Previous works have used Eulerian Video Magnification (EVM) in conjunction with haar-cascades for face detection which gave misleading results. In this paper, we have proposed a special Convolutional Neural Network (CNN) model for face detection on which EVM is applied for amplifying the micro-expressions to a calculated threshold. Following that, a separately trained CNN is used to classify the formerly detected micro-expression into one of the seven universal micro expressions. Results obtained during the test experiment are presented at the end of the paper.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".