Facial expression recognition (FER) is an important task to improve cross-domain human-computer interaction systems. Given the dataset provided by the competition organizer, we modified and applied the current state-of-the-art convolutional neural networks (CNNs) to the FER task. Our best single model obtain a 69.1% in the validation accuracy, while our ensemble model achieves 71.1%. Additionally, we investigated the quality of the models and the limits of the given dataset.
Read the report.pdf for more details on the work.
Install first the dependencies, then
python3 webcam.py
This will activate the webcam and uses a simple Haar Cascade model from OpenCV to detect the face from the video frames. The detected region will be fed to the FER model for emotion classification.
Results of the experiments on the dataset FER2013 and the competition dataset FERLoopAI.
| Architecture | Parameters | FER2013 Valid Acc. | FERLoopAI Valid Acc. |
|---|---|---|---|
| Human (Base) | - | 65 | - |
| VGGNet* | 4.7 M | 72.6 | 69.1 |
| ResNet18* | 5.1 M | 72.2 | 67.7 |
| DenseNet121* | 3.9 M | 70.6 | 67.2 |
| ViT | 85.2 M | 64.2 | 63.8 |
| MobileNetV3 | 5.4 M | - | 61.8 |
| EfficientNet-B3 | 10.7 M | - | 66.6 |
| Ensemble5 | 115 M | - | 71.1 |