Day 5 - Face Feature Extraction with OpenCV (dlib)

Welcome to Day 5 of our Face Recognition Series! In this lesson, we’ll dive deep into Face Feature Extraction using OpenCV and dlib — two powerful libraries in computer vision. You’ll learn how to: Detect facial landmarks (eyes, nose, mouth, jawline, etc.) Extract and visualize key facial features Understand how feature points are used for facial recognition and alignment Implement feature extraction step-by-step in Python By the end of this video, you’ll be able to extract and analyze facial features that can be used in face recognition, emotion detection, and tracking applications. 📘 Topics Covered: Installing and importing dlib and OpenCV Loading pre-trained face landmark models Detecting faces and facial landmarks Drawing and visualizing facial feature points Using extracted features for further ML/AI tasks 🧠 Project Context: This lesson is part of our AI and ML Enthusiast Course: Building a Face Recognition Web App with Django, Machine Learning, and Cloud Deployment on AWS. 📂 Source Code & Resources: 👉 [Add your GitHub or course materials link here] 📺 Previous Lessons: Day 1: Introduction to AI & ML Day 2: OpenCV Crash Course Day 3: Face Detection with OpenCV Day 4: OpenCV Face Detection with DNN 🔔 Subscribe to stay updated — more exciting lessons on Computer Vision and Face Recognition are coming soon!