Two new methods for facial expression recognition using Convolutional Neural Networks
Bibliographic record
Abstract
Abstract In this research, we propose two novel methods for facial expression recognition to improve the accuracy of recognition. The first of our novel approach is to add the Batch Normalization (BN) layer to the CNN model, and the second of the novel approach is to preprocess the image before image training, such as rotating image, cropping the image and adding Gaussian noise in the picture, especially it is beneficial for unbalanced classifications. Our model consists of 3 CNN layers, 3 BN layers, three average-pooling layers, and three fully-connected layers; our model has a satisfying performance on the prediction category after adopting the two methods mentioned above. Our CNN model is trained and tested with Kaggle facial expression recognition challenge databases. The implemented system can automatically recognize seven expressions in real-time: anger, disgust, fear, happiness, neutral, sadness, and sur-prise. The experimental results demonstrate the effectiveness of our proposed approach.
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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 | 0.002 |
| 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".