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Code for training an emotion recognition model using CNNs on the FER2013 dataset. Includes data preprocessing, CNN architecture building with Keras, model training, evaluation, and visualization of accuracy. Ideal for developing emotion recognition systems.

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SuranSandeepa/KiddoCare-Hub-Facial-Emotion-Recognition

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KiddoCare-Hub-Facial-Emotion-Recognition

Emotion Recognition Model Overview This repository contains the code for an emotion recognition model built using Convolutional Neural Networks (CNN). The model is trained to recognize emotions from facial expressions using the FER2013 dataset.

Technologies Used

  • Python
  • Keras
  • NumPy
  • pandas
  • matplotlib
  • scikit-learn

Dataset

The model is trained on the FER2013 dataset, which includes facial expression images categorized into seven emotions.

CNN Architecture

The CNN consists of multiple convolutional layers, max-pooling layers, dropout layers, and fully connected layers. Training and validation accuracy are visualized using matplotlib.

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Code for training an emotion recognition model using CNNs on the FER2013 dataset. Includes data preprocessing, CNN architecture building with Keras, model training, evaluation, and visualization of accuracy. Ideal for developing emotion recognition systems.

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