Face Detection and Emotion Recognition Project

This project involves a face detection system with emotion recognition capabilities. The system uses a pre-trained neural network to identify emotions from facial expressions captured through a webcam.

Introduction

This project utilizes OpenCV for face detection and a Convolutional Neural Network (CNN) for emotion recognition. The model is trained to recognize seven different emotions: angry, disgust, fear, happy, neutral, sad, and surprise.

Prerequisites

Before running the project, ensure you have the following:

• Python 3.x
• OpenCV
• Keras
• NumPy
• tensorflow
• pandas
• jupyter
• tqdm
• opencv-contrib-python

Code Overview

Here is a brief overview of the main code components:

• Loading the Model
• Feature Extraction
• Webcam Initialization
• Emotion Detection Loop

Dataset Preparation

To create a dataset for training, you can organize your images into separate directories for each emotion. Here is a simple script to create a DataFrame from your image directories:

Advantages

• Real-time Processing: The system can process and recognize emotions in real-time using webcam input.
• Pre-trained Model: Utilizes a pre-trained model, making it easier to deploy without extensive training.
• Extensible: The system can be extended to recognize more emotions or perform additional tasks such as age and gender detection.
• Educational: Provides a practical application of machine learning and computer vision techniques.

Acknowledgements

This project uses the following libraries and frameworks:

• OpenCV
• Keras
• TensorFlow
• NumPy
• pandas

Conclusion

This face detection and emotion recognition project showcases the power of combining computer vision and machine learning techniques. By utilizing OpenCV and a pre-trained CNN, the system can accurately detect faces and recognize emotions in real-time. This project can serve as a foundation for more advanced applications in fields such as human-computer interaction, security, and behavioral analysis.