Facial Expression Recognition using Deep Learning

Project Overview: The goal of this project is to develop a deep learning model capable of accurately recognizing facial expressions from images. Utilizing popular deep learning architectures and preprocessing techniques, this project explores the effectiveness of different models and strategies in facial expression recognition (FER).

Objectives

1. Apply deep learning techniques to recognize facial expressions from images accurately.
2. Utilize data preprocessing and augmentation strategies to improve model performance.
3. Explore the application of transfer learning with pre-trained models like VGG16, ResNet50, MobileNetV2, and Xception for FER.

Features

1. Pre-trained Model Integration:

• Incorporates pre-trained models like VGG16, ResNet50, MobileNetV2, and Xception using transfer learning techniques.
• Fine-tuning and adaptation of these models to the specific task of facial expression recognition.

2. Data Preprocessing and Augmentation:

• Utilizes OpenCV libraries for face detection and alignment.
• Employs data augmentation techniques such as the addition of Gaussian noise and batch normalization to enhance the training process.

3. Hyperparameter Tuning:

• Adjusts hyperparameters like learning rate and implements early stopping based on validation set performance.

4. Comprehensive Experiments:

• Conducts extensive experiments to evaluate the effectiveness of different model variations, fine-tuning strategies, and preprocessing techniques.

5. Detailed Analysis and Documentation:

• Provides in-depth analysis of experiment results, offering insights into the most effective strategies for FER.

Technology Stack

• Deep Learning Frameworks: TensorFlow, Keras for model training and evaluation.
• Computer Vision: OpenCV for image processing and face detection.
• Dataset: FER2013 for training and testing the models.

Outcome

The project has significantly advanced the state of FER systems, demonstrating the potential of deep learning techniques in recognizing a wide range of facial expressions with high accuracy. It serves as a valuable resource for further research and development in the field of computer vision and human-computer interaction.

Getting Started

To explore this project, clone the repository, and refer to the documentation for setup and execution instructions. Detailed guides on model training, dataset preparation, and evaluation are included.

For more details about the project and its findings, please check the code files and plots folder in this repository.

Note: This documentation is designed to provide a comprehensive overview of the project’s approach and outcomes, suitable for developers, researchers, and students interested in deep learning and facial expression recognition.