Deep Learning-Based Micro Facial Expression Recognition Using an Adaptive Tiefes FCNN Model
Bibliographic record
Abstract
The scientific community and media have increasingly recognized the significance of microexpressions as indicators for detecting deception, as they reveal genuine emotions that individuals attempt to conceal.To capitalize on these subtle cues of deceit, researchers have developed applications capable of automatically detecting and recognizing microexpressions, which are typically imperceptible to the human eye.Facial expressions serve as fundamental ground truth determinants in multimedia applications.Earlier models, such as GA, RFO, X-Boosting, and Gradient Boosting, demonstrate greater efficiency in terms of time and accuracy.However, not all applications are capable of detecting micro facial expressions.In this study, a deep learning-based Tiefes FCNN model is designed specifically for micro facial expression recognition.Implemented using Python software, the proposed model consists of two stages: first, pre-processing is performed using image segmentation, followed by the application of a deep learning model employing Tiefes FCNN technology in the second stage.The experimental results exhibit significant performance improvements, including an accuracy of 99.02%, precision of 98.82%, F1-score of 97.8%, PSNR of 56.31, and CC of 96.31.
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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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".