Emotion Detection Using CNN and FER-2013 Dataset

Overview

This project focuses on detecting emotions from facial images using Convolutional Neural Networks (CNNs). The goal was to create a robust emotion detection model by addressing class imbalance in the FER-2013 dataset and employing advanced CNN architectures such as VGG16 and ResNet50v2. This README will cover the business problem, the problem statement, the solution approach, and key learnings from the project.

Business Problem

Emotion detection has significant applications in various fields such as human-computer interaction, healthcare, marketing, and security. Accurate emotion detection can enhance user experiences, provide better mental health assessments, and improve security measures by detecting unusual emotional states.

Problem Statement

The objective of this project was to develop an end-to-end solution for detecting emotions from facial images using the FER-2013 dataset. The key challenges included:

• Addressing class imbalance in the dataset
• Improving model robustness and accuracy
• Leveraging transfer learning for enhanced performance

Solution Approach

Data Preprocessing

1. Handling Class Imbalance:
   • Applied data augmentation techniques to generate new training samples by making slight modifications to existing images, such as flipping, rotating, and adjusting brightness. This helped in balancing the dataset and improving the model's robustness.

Model Development

1. Initial Model:

   • Started with a simple CNN architecture, but the results were not satisfactory due to class imbalance and limited data diversity.

2. Advanced Models:

   • Designed and iterated on custom CNN models, including advanced architectures such as VGG16 and ResNet50v2, to optimize performance.

3. Transfer Learning:

   • Leveraged pre-trained models like VGG16 and ResNet. Fine-tuning these models for the emotion detection task significantly improved the accuracy and performance of the model.

Key Learnings

• Handling Data Imbalance:

  • Understood the importance of addressing data imbalance and how data augmentation can be a powerful technique to enhance the diversity of training data.

• Transfer Learning:

  • Gained hands-on experience with transfer learning and how pre-trained models can be adapted to new tasks, saving training time and boosting model performance.

Technologies Used

• Python
• TensorFlow
• Keras
• OpenCV

Results and Performance

• The final model achieved improved accuracy and robustness in detecting emotions from facial images.
• Employed advanced CNN architectures and transfer learning to optimize model performance.

Conclusion

This project provided valuable insights into handling class imbalance, data augmentation, and transfer learning in the context of emotion detection. The techniques and models developed can be applied to other similar image classification tasks, demonstrating the versatility and power of CNNs and transfer learning.