Body-Language-Detection-with-MediaPipe-and-OpenCV
| Property | Value |
|---|---|
| Author | ThisIs-Developer |
| Model URL | https://huggingface.co/ThisIs-Developer/Body-Language-Detection-with-MediaPipe-and-OpenCV |
| Framework | MediaPipe, OpenCV, Scikit-learn, TensorFlow-Keras |
What is Body-Language-Detection-with-MediaPipe-and-OpenCV?
This innovative system combines MediaPipe and OpenCV technologies with dual model architecture for comprehensive body language and emotion detection. It utilizes both Scikit-learn (.pkl) and TensorFlow-Keras (.tflite) models to achieve high-accuracy recognition across 10 distinct emotional categories.
Implementation Details
The system implements two parallel models: a Scikit-learn pipeline achieving up to 99.5% accuracy using various classifiers (LogisticRegression, RidgeClassifier, RandomForestClassifier, and GradientBoostingClassifier), and a TensorFlow-Keras neural network optimized for mobile deployment through TFLite conversion.
- Dual model architecture with Scikit-learn and TensorFlow-Keras implementation
- Real-time processing capability through webcam integration
- Support for MP4 video analysis
- Comprehensive emotion recognition across 10 categories
- Visual analytics through multiple plot types (pie, bar, horizontal bar)
Core Capabilities
- Recognition of 10 emotional states: Happy, Sad, Angry, Surprised, Confused, Tension, Surprised, Excited, Pain, Depressed
- Real-time video processing through webcam feed
- MP4 video file analysis support
- Advanced visualization capabilities
- High accuracy rates (up to 99.5% with certain classifiers)
Frequently Asked Questions
Q: What makes this model unique?
This model's dual architecture approach, combining traditional machine learning with deep learning, provides robust emotion recognition capabilities while maintaining deployment flexibility through TFLite optimization.
Q: What are the recommended use cases?
The model is ideal for real-time emotion recognition in video feeds, human-computer interaction applications, psychological research, and automated emotion analysis in recorded videos.
