Facial Emotion Detection Project

Overview

This project is a facial emotion detection system that uses deep learning techniques to recognize and classify human emotions based on facial expressions. It is designed to provide a reliable and efficient solution for detecting emotions such as happiness, sadness, anger, and more in real-time or from images.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Model Training

Features

• Real-time emotion detection from live video streams.
• Emotion classification for individual images.
• Supports multiple emotions, including happiness, sadness, anger, and more.

Installation

To use this facial emotion detection system, follow these installation steps:

1. Clone the repository:

   git clone https://github.com/tajammulbasheer/facial_emotion_detection.git

2. Navigate to the project directory:

   cd facial_emotion_detection

3. Install the required Python packages using pip:

   pip install -r requirements.txt

Usage

You can use the facial emotion detection system as a standalone application or integrate it into your own projects. Here's how to run the system:

1. To perform real-time emotion detection, run the following command:

   python predict_oncam.py --model_path 'path to saved model'

2. To classify the emotion in an individual image, use the following command:

   python predict_onimage.py --model_path 'path to saved model' --image your_image.jpg

Model Training

If you want to train your own emotion detection model, follow these steps:

1. Prepare your dataset of facial expressions with corresponding emotion labels.

2. Modify the model architecture in train_build_model.py to suit your requirements.

3. Train your model using your dataset:

   python train_build_model.py --data_path /path/to/your/dataset

4. After training, save the model weights and update the configuration in the code to use your custom model.