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---
license: mit
library_name: keras
tags:
- image-classification
- keras
- face
- face-expression
- facial-expression
---
# Facial expression classification model
This model is designed for facial expression classification and it uses custom CNN model to classify the images into 7 different categories.
This CNN Model is to classify the facial expression into one of the following categories:
1. Anger
2. Disgust
3. Fear
4. Happiness
5. Neutral
6. Sadness
7. Surprise
## Architecture Summary
| Layer (type) | Output Shape | Param # |
|--------------------------------------|-----------------------------|-----------------|
| conv2d (Conv2D) | (None, 46, 46, 32) | 320 |
| max_pooling2d (MaxPooling2D) | (None, 23, 23, 32) | 0 |
| dropout (Dropout) | (None, 23, 23, 32) | 0 |
| conv2d_1 (Conv2D) | (None, 21, 21, 64) | 18,496 |
| max_pooling2d_1 (MaxPooling2D) | (None, 10, 10, 64) | 0 |
| batch_normalization (BatchNormalization) | (None, 10, 10, 64) | 256 |
| dropout_1 (Dropout) | (None, 10, 10, 64) | 0 |
| conv2d_2 (Conv2D) | (None, 8, 8, 128) | 73,856 |
| max_pooling2d_2 (MaxPooling2D) | (None, 4, 4, 128) | 0 |
| batch_normalization_1 (BatchNormalization) | (None, 4, 4, 128) | 512 |
| dropout_2 (Dropout) | (None, 4, 4, 128) | 0 |
| conv2d_3 (Conv2D) | (None, 2, 2, 128) | 147,584 |
| flatten (Flatten) | (None, 512) | 0 |
| dense (Dense) | (None, 96) | 49,248 |
| dropout_3 (Dropout) | (None, 96) | 0 |
| dense_1 (Dense) | (None, 96) | 9,312 |
| dropout_4 (Dropout) | (None, 96) | 0 |
| dense_2 (Dense) | (None, 64) | 6,208 |
| dense_3 (Dense) | (None, 7) | 455 |
Total params: 306,247 (1.17 MB)
Trainable params: 305,863 (1.17 MB)
Non-trainable params: 384 (1.50 KB)
## Training details
- Dataset: https://www.kaggle.com/datasets/manishshah120/facial-expression-recog-image-ver-of-fercdataset
| Name | Value |
|------|-------|
| Input shape | 48x48 (48, 48, 1) |
| Optimizer | Adam |
| Loss | Crossentropy |
| Max epochs | 200 |
| Early stopping monitor | val_loss |
| Early stopping patience | 12 |
## Model performance
- Training Accuracy: 0.5758 (Epoch #84)
- Training Loss: 1.1272 (Epoch #84)
- Validation Accuracy: 0.5823 (Epoch #84)
- Validation Loss: 1.1285 (Epoch #84)
### Classification report
```
precision recall f1-score support
0 0.52 0.40 0.45 491
1 0.00 0.00 0.00 55
2 0.43 0.17 0.25 528
3 0.83 0.84 0.83 879
4 0.51 0.67 0.58 626
5 0.39 0.58 0.47 594
6 0.73 0.72 0.73 416
accuracy 0.58 3589
macro avg 0.49 0.48 0.47 3589
weighted avg 0.58 0.58 0.57 3589
```
## Notebook
Training notebook: https://www.kaggle.com/code/harkishankhuva/facial-expression-classification
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