Portfolio Details

Each project in this portfolio reflects my hands-on experience in solving real-world problems using data science, machine learning, and full-stack development. From predictive models to intelligent web applications, these solutions are built with a focus on scalability, functionality, and impact.

Project Information

  • Project Name: Real-time Facial Emotion Detection
  • Category: Computer Vision & Deep Learning
  • Model Type: Custom CNN (Convolutional Neural Network)
  • Accuracy: 90% Training, 65.9% Validation
  • Framework: TensorFlow/Keras
  • Github Repo: Link

Technologies Used

This emotion detection system uses a custom-trained CNN model built with TensorFlow/Keras, achieving 90% training accuracy. It leverages OpenCV for real-time face detection and MediaPipe for facial landmark extraction. The model classifies 7 core emotions (angry, disgust, fear, happy, neutral, sad, surprise) and runs efficiently on standard webcams.

Details of Project

Understanding human emotions through facial expressions has applications from mental health to human-computer interaction. This project delivers real-time emotion analysis with a lightweight deep learning model that balances accuracy and performance.

The system captures video frames, detects faces using computer vision techniques, extracts facial landmarks as features, and classifies emotions using our custom-trained neural network. Despite the complexity of emotion recognition, our model achieves strong performance with optimized architecture and careful training.

Key Technical Features

  • Custom CNN architecture optimized for real-time performance (90% training accuracy)
  • Facial landmark extraction using MediaPipe for robust feature detection
  • 7-class emotion classification (angry, disgust, fear, happy, neutral, sad, surprise)
  • Efficient processing at 15-20 FPS on standard hardware
  • Modular design allowing for model improvements and extensions
  • Clean Python implementation with OpenCV for video processing

Potential Applications

  • Mental health monitoring tools
  • Human-computer interaction systems
  • Customer experience analytics
  • Educational technology for engagement measurement
  • Accessibility tools for emotion recognition