Date: 30-04-2024
Author: Rizwan
This project builds a real-time emotion detection system using facial recognition and machine learning to identify various emotions from webcam feeds. It displays the detected emotions with corresponding images and names on the screen. Additionally, a dynamic score bar graph shows confidence levels for each detected emotion, enhancing user interaction and system understanding. The system is trained to modulate webcam images based on the subject's expressions in real time and pre-recorded videos using a variety of emotions including 'angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', and 'surprise'.
The model is trained on the FER dataset from Kaggle, containing various facial expressions categorized into seven classes. For more information and access to the dataset, visit the FER2013 Dataset on Kaggle.
Training Set:
| Emotion | Number of Images |
|---|---|
| Angry | 3993 images |
| Disgust | 436 images |
| Fear | 4103 images |
| Happy | 7164 images |
| Neutral | 4982 images |
| Sad | 4938 images |
| Surprise | 3205 images |
Test Set:
| Emotion | Number of Images |
|---|---|
| Angry | 960 images |
| Disgust | 111 images |
| Fear | 1018 images |
| Happy | 1825 images |
| Neutral | 1216 images |
| Sad | 1139 images |
| Surprise | 797 images |
This system uses a sophisticated machine learning model built on the ResNet50V2 architecture, pre-trained on ImageNet and adapted for emotion detection. It includes features such as Batch Normalization, Global Average Pooling2D, and Dense layers with Dropout, culminating in a softmax activation function for classifying the seven emotions.
- Training Accuracy: Improved from 21.2% to 63.3% over 50 epochs.
- Validation Accuracy: Increased from 23.7% to 66.6%.
# Create a Conda environment
conda create --name facial_emotion_recognition python=3.10
# Activate the Conda environment
conda activate facial_emotion_recognition
# Clone the repository
git clone https://github.com/Rizwanali324/Facial_Recognation-Emotions_Classification.git
cd facial_emotion_recognition
# Install the project dependencies
pip install -r requirements.txt
**Running the Code**
python code/emotion.py
**Converting the Model to TFLite**
python code/converter.py
To enhance model performance and robustness:
- Dataset Expansion: Include a broader range of facial expressions and real-world conditions.
- Multimodal Data Integration: Incorporate audio cues alongside visual data.
- Advanced Neural Architectures: Explore deep convolutional networks, recurrent neural networks, or attention-based models.
This emotion detection project aims to enhance human-computer interaction by enabling computers to interpret human emotions accurately, demonstrating promising applications in various real-world settings.