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Computer Visionadvanced

Facial Emotion Detection System

This project focuses on detecting various emotions including neutral, angry, disgust, fear, happy, sad, and surprise, through the use of computer vision technologies. By analyzing facial expressions, it aims to provide valuable insights applicable in diverse real-world scenarios, enhancing understanding in fields such as psychology, security, and human-computer interaction.

9 lectures

What You Will Learn

Master the end-to-end Machine Learning project lifecycle from data collection to cloud deployment.
Implement real-time facial emotion detection using the YOLOv11 deep learning model.
Understand how to fine-tune pre-trained YOLOv11 models on custom datasets for facial emotion recognition.
Gain experience building RESTful web applications with FastAPI for serving ML model predictions.
Develop and deploy Dockerized applications on AWS cloud infrastructure (ECR & EC2).

System Architecture

Facial Emotion Detection System Architecture Diagram

High-level architecture overview of the Facial Emotion Detection System .

What You'll Build

  • A "Facial Emotion Detection System" capable of detecting and classifying human emotions (Angry, Disgust, Fear, Happy, Neutral, Sad, Surprise) from images and live webcam feeds.
  • A YOLOv11-powered detection engine fine-tuned on custom facial emotion datasets with optimized inference parameters.
  • A FastAPI-based backend API that accepts image uploads and webcam frames, runs YOLOv11 inference, and returns annotated detection results.
  • A user-facing web application with image upload and live webcam detection capabilities using a custom HTML/CSS/JavaScript frontend.
  • A fully automated CI/CD pipeline that builds Docker images, pushes to AWS ECR, and deploys on AWS EC2.

Project Instructor

Boktiar Ahmed Bappy

Boktiar Ahmed Bappy

5+ years exp
LinkedIn
Facial Emotion Detection System
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