Emotion Detection Using Deep Learning

This project focuses on detecting human emotions from facial images using Convolutional Neural Networks (CNNs).

Data Set

https://www.kaggle.com/datasets/jonathanoheix/face-expression-recognition-dataset/data

• Format: 48x48 pixel grayscale images
• Classes:
  • Angry
  • Disgust
  • Fear
  • Happy
  • Sad
  • Surprise
  • Neutral

The dataset used to predict the emotion is gathered from kaggle

🧠 Model Architecture

The model is built using TensorFlow/Keras and consists of:

• Convolutional layers with ReLU activation
• MaxPooling layers for dimensionality reduction
• Dropout for regularization
• Dense (fully connected) layers
• Softmax activation in the output layer for classification

Optionally, models like VGG16 or ResNet50 can be used for transfer learning.

⚙️ Dependencies

tensorflow
numpy
matplotlib
pandas
scikit-learn
opencv-python

Accuracy

There is a slight ups and down in the accuracy because of the Overfitting

You can also check this project on kaggle with the dataset and other response to it

https://www.kaggle.com/code/pariveshrohilla4105/emotion-detection

A Brief OverView OF the Images of Dataset