Facial Emotion Recognition Project 🧐

This project uses a deep learning model to perform real-time facial emotion recognition. It can identify faces within an image and classify the emotion of each face into one of seven categories: happy, surprise, sad, anger, disgust, fear, or neutral.

✨ Features

• Face Detection: Uses OpenCV's highly reliable Haar Cascade classifier to locate faces in an image.
• Emotion Classification: Employs a pre-trained PyTorch model (ResEmoteNet) based on a ResNet architecture to classify emotions.
• Visual Feedback: Draws bounding boxes around detected faces and labels them with the predicted emotion.

🛠️ Technology Stack

• Python 3.8+
• PyTorch: The deep learning framework used for the model.
• OpenCV: For image processing and face detection.
• Pillow (PIL): For image manipulation and transformations.
• NumPy: For numerical operations.

🚀 Getting Started

To get this project running on your local machine, please follow the detailed setup and execution instructions in the challenge.md file.