🎭 Emotion Detection via Facial Expressions & Body Gestures

A real-time emotion recognition system using MediaPipe and Machine Learning, capable of detecting and classifying human emotions based on facial expressions, hand gestures, and body posture.

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📌 Project Overview

This project combines:

• MediaPipe Holistic for extracting body, face, and hand landmarks
• A trained ML model for emotion classification
• OpenCV for real-time webcam processing and display

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🎯 Goals

• Detect facial and body landmarks in real time
• Extract 2,130 keypoint features per frame
• Predict human emotions like happy, sad, angry, surprised, etc.
• Visualize both prediction and facial/pose mesh

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🧠 Model Details

The model uses:

• Pose Landmarks: 33 points × 4 features = 132
• Face Landmarks: 468 points × 4 features = 1,872
• Left & Right Hand: 21 × 3 × 2 = 126
  ➡️ Total: 2,130 features

Trained model is stored in body_language.pkl.

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📸 Results

Here are some sample outputs from the emotion recognition system:

🛠️ Requirements

Make sure you have Python 3.7+. Install dependencies using:

pip install -r requirements.txt