Regularizing the loss layer of CNNs for facial expression recognition using crowdsourced labels
Bibliographic record
Abstract
Deep, convolutional neural networks have become the state-of-the-art method for automatic Facial Expression Recognition (FER). Because of the small size and controlled conditions of most FER datasets, however, models can still overfit to the training dataset and struggle to generalize well to new data. We present a novel approach of using crowdsourced label distributions for improving the generalization performance of convolutional neural networks for FER. We implement this as a loss layer regularizer, where the ground truth labels are combined with crowdsourced labels in order to construct a noisy output distribution during training. We use a label disturbance method in which training examples are randomly replaced with incorrect labels drawn from the combined label probability distribution. We compare the performance of our disturbed and undisturbed models in cross-validation testing on the extended Cohn-Kanade dataset and cross-dataset experiments on the MMI, JAFFE, and FER2013 datasets. We find that using our proposed method, test performance is improved on both the MMI and JAFFE datasets. Our results suggest that using nonuniform probability distributions to disturb training can improve generalization performance of CNNs on other FER datasets.
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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 | 0.001 | 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) | 0.001 | 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 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".