---
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