Facial Expression Recognition Using Deep Learning and Neural Embeddings
Bibliographic record
Abstract
This study investigates Facial Expression Recognition (FER) as essential for understanding human emotions conveyed through facial expressions, involving face detection, facial expression detection, and classification.Recent advancements in deep learning have significantly enhanced FER accuracy, exemplified by combining Visual Geometry Group (VGG) and U-Net segmentation layers, achieving a remarkable 75.97% accuracy.Building upon prior research on neural embeddings, this study explores their application in improving FER models, focusing on basic models like VGG-19 and employing triplet loss.Extracted features are classified using various methods such as Support Vector Machine, XGBoost, Random Forest, and Artificial Neural Networks, with evaluation metrics including accuracy, precision, recall, and F1 Score.Findings indicate that modifications to the VGG19 classifier improve accuracy, with XGBoost attaining the highest accuracy of 65.70%.However, integrating triplet loss does not yield significant improvement, recording a highest accuracy of 65.30% when combined with the XGBoost model.These results suggest potential limitations, such as incorrect distance calculation methods and dataset imbalance, which need addressing for enhancing model efficacy and real-world applicability.Therefore, future research should focus on refining distance calculation techniques and ensuring dataset balance.
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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".