This project is a face emotion detection system that utilizes computer vision and machine learning techniques to analyze and recognize emotions displayed on human faces. It provides a robust and accurate method for automatically detecting and classifying emotions such as happiness, sadness, anger, surprise, fear, and disgust.
• This repository contains a deep learning-based solution for emotion detection using Convolutional Neural Networks (CNN). The system analyzes facial expressions in images and classifies them into different emotion categories such as happiness, sadness, anger, surprise, fear, and disgust.
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CNN Architecture: The core of the system is a CNN model specifically designed for emotion detection. It leverages the power of deep learning to extract high-level features from facial images and make accurate emotion predictions.
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Data Preprocessing: The repository includes code for data preprocessing, which involves transforming and augmenting the dataset to enhance the model's ability to generalize. It ensures robustness across various facial expressions, lighting conditions, and demographic factors.
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Training and Evaluation: The system provides scripts and utilities to train the CNN model using labeled facial expression datasets. It includes options to customize hyperparameters, such as learning rate, batch size, and number of training epochs. Additionally, evaluation tools are available to assess the model's performance on validation or test datasets.
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Real-Time Emotion Detection: The repository contains a real-time emotion detection script that utilizes the trained model to detect emotions in live video streams from a webcam. It employs computer vision techniques to detect faces, extract facial regions, and predict emotions frame by frame.
Follow the steps below to get started with the emotion detection system:
Clone the Repository: Start by cloning this repository to your local machine using the following command:
bash
git clone https://github.com/your-username/Face_Emotion_Detection_CNN.git
Install Dependencies: Navigate to the repository's directory and install the required dependencies using the following command:
pip install -r requirements.txt
Dataset Preparation: Obtain a labeled facial expression dataset for training the emotion detection model. Ensure that the dataset is organized according to the repository's expected format.
Data Preprocessing: Use the provided data preprocessing scripts to preprocess and augment the dataset. Adjust the preprocessing parameters to fit your requirements.
Training: Train the emotion detection model by running the training script. Customize the hyperparameters and paths to the dataset as needed.
Evaluation: Evaluate the trained model's performance by running the evaluation script on a validation or test dataset.
Real-Time Emotion Detection: Use the real-time emotion detection script to detect emotions in live video streams. Ensure that a webcam is connected to your system.
Conv2D
MaxPooling2D
Dense
Dropout
Flatten
click Here link for download Dataset : `https://www.kaggle.com/datasets/jonathanoheix/face-expression-recognition-dataset/download?datasetVersionNumber=1`
Contributions to this project are welcome. If you encounter any issues or have ideas for improvements, please open an issue or submit a pull request. Make sure to adhere to the project's code style and guidelines.
This project is licensed under the MIT License. Feel free to use, modify, and distribute the code for both non-commercial and commercial purposes.
Keras
Tensorflow
Scikit-Learn
Numpy
Pandas
os
matplotlib
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Human-Computer Interaction: The system can be utilized in various human-computer interaction scenarios, such as emotion-aware interfaces, virtual reality experiences, and adaptive user interfaces.
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Market Research and User Experience Testing: Researchers and companies can employ the system to collect data on user emotional responses during product testing, marketing campaigns, or user experience evaluations.
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Mental Health Monitoring: The project can be adapted for applications in mental health, assisting in emotion tracking and analysis for individuals with conditions like depression, anxiety, or autism.
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Entertainment and Gaming: The system can enhance gaming experiences by enabling games to respond dynamically based on the player's emotional state, creating more immersive and personalized gameplay.
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Security and Surveillance: The face emotion detection system can be integrated into security and surveillance systems to detect suspicious or potentially dangerous individuals based on their emotional expressions.