This project implements a computer vision system that first detects faces using cascade detectors and then classifies facial expressions using a convolutional neural network (CNN). The system can identify different emotional states from facial images in real-time.
Key Features
- Face Detection: Uses OpenCV cascade detectors to locate faces in images or video streams
- Expression Classification: CNN model trained to recognize different emotional expressions
- Real-time Processing: Capable of processing video input for immediate emotion detection
- Multiple Expression Categories: Identifies various emotional states from facial images
Technical Implementation
The project follows a two-stage pipeline:
- Face Detection: Utilizing Haar cascade classifiers to identify and extract face regions
- Expression Classification: Feeding the detected faces into a CNN model trained on facial expression datasets
Technologies
- Python
- OpenCV
- TensorFlow/Keras
- Convolutional Neural Networks
- Computer Vision
- Cascade Detection
Applications
- Human-Computer Interaction: Enhance user experience by responding to emotional states
- User Experience Testing: Analyze user emotional responses to interfaces or content
- Security Systems: Add emotional context to facial recognition systems
- Behavioral Analysis: Study emotional responses in various environments
- Accessibility: Create assistive technologies for individuals with difficulty expressing emotions
This project demonstrates the practical application of deep learning and computer vision techniques to recognize human emotions, opening up numerous possibilities for emotion-aware applications.