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this is the final project: Facial Expressions Detection using Compact Convolutional Transformers(CCT)
Description: The aim of this project is to design and implement a system capable of accurately detecting and classifying human facial expressions using Compact Convolutional Transformers (CCT). By utilizing a dataset of facial expressions, the system will classify images into key emotional categories such as happiness, sadness, anger, surprise, fear, and neutrality. The CCT architecture uniquely combines convolutional layers for extracting detailed local features with transformer-based layers to capture global patterns, making it particularly well-suited for this task.
Project Workflow:
1. Data Collection
We will use an existing publicly available dataset, such as the FER2013 dataset from Kaggle, which includes a variety of labeled facial expressions.
2.Data Organization
The dataset will be structured into folders representing each emotion category (e.g., Happy, Sad, Angry, etc.). This hierarchical organization will make it easier to preprocess the data and load it into the model. The dataset will also be split into training, validation, and testing subsets to allow for effective model evaluation
3.Preprocessing
To prepare the data for training:
• Images will be converted to grayscale, reducing complexity and focusing on the structural features of the face.
• All images will be resized to a consistent dimension of 48×48 pixels to match the input requirements of the model.
4. Model Development
The Compact Convolutional Transformer (CCT) architecture will be implemented to leverage the strengths of both convolutional and transformer-based models:
• Convolutional layers will focus on identifying local features, such as edges and facial landmarks.
• Transformer layers will capture global relationships across the face, enabling the model to understand context and subtle variations in expressions.
• A classification head will output predictions for the six target emotional categories.
5.Training and Validation
The dataset will be divided into 70% training, 20% validation, and 10% testing. During training, we will monitor metrics such as accuracy, precision, recall, and F1-score to track the model’s performance. Techniques like data augmentation, learning rate scheduling, and regularization will be employed to enhance the model’s generalization ability and prevent overfitting.
6. Testing and Evaluation
Once trained, the model will be evaluated on both the testing subset and unseen data to measure its robustness. This process will help ensure that the model can handle real-world variability in facial expressions.
Expected Outcome
By the end of this project, we aim to create a robust system capable of detecting and classifying facial expressions with high accuracy.
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