abhay06102003/emotiondectection

Emotion Detection with Deep Learning

This project aims to detect emotions in facial expressions using deep learning techniques. It utilizes the FER2013 dataset for training, and the model architecture is based on the Xception neural network.

Introduction

Emotion detection has various applications, including sentiment analysis, human-computer interaction, and mental health assessment. This project explores the use of deep learning to automatically recognize emotions from facial expressions.

Dataset

The project utilizes the FER2013 dataset, which contains images of faces categorized into seven different emotions: angry, disgust, fear, happy, sad, surprise, and neutral. The dataset is split into training, validation, and testing sets.

Methodology

1. Data Preprocessing: Images are resized and normalized to match the input requirements of the Xception model.

2. Model Architecture: The Xception model is used as the base, with additional layers added for fine-tuning. The final layer consists of a softmax activation function for multi-class classification.

3. Training: The model is trained using the training set, with validation performed on a separate validation set. The training process includes 20 epochs.

4. Evaluation: Model performance is evaluated using accuracy metrics on the validation set.

Requirements

• Python 3
• TensorFlow
• Keras
• OpenCV
• NumPy
• Pandas
• Scikit-learn

Usage

1. Clone the repository:

git clone https://github.com/your_username/emotion-detection.git

2. Install the required dependencies:

pip install -r requirements.txt

Future Work

• Explore techniques for improving model accuracy.
• Deploy the model for real-time emotion detection applications.
• Experiment with other datasets and pre-trained models for comparison.

Credits

• FER2013 Dataset - Kaggle
• Xception Model - Keras Documentation

License

This project is licensed under the MIT License.