This project is a Convolutional Neural Network (CNN) built using TensorFlow and Keras to classify facial expressions into two categories: Happy and Sad. It utilizes data augmentation and transfer learning techniques for better performance and generalization.
Facial emotion recognition is a key component in human-computer interaction. This project demonstrates how deep learning can be used to distinguish between happy and sad faces by training a CNN model on a custom dataset.
The goal is to build a model that:
- Accepts facial images as input
- Classifies them as Happy or Sad
- Achieves high accuracy using real-world-like data and basic data augmentation
- Image preprocessing using
ImageDataGenerator - CNN architecture with Conv2D, MaxPooling, Flatten, Dense layers
- Model checkpointing to save the best version
- Early stopping to prevent overfitting
- Visualization of training and validation performance
- Clone the repository:
git clone https://github.com/your-username/image-Classifier.git
cd image-Classifier