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Build Real-Time Face Recognition with OpenCV

This course is part of Apply OpenCV for Real-Time Computer Vision Projects Specialization

Instructor: EDUCBA

Included with

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2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Explain computer vision basics and apply edge detection techniques using OpenCV.
Build facial image datasets and train classifiers for face recognition tasks.
Develop real-time face and eye recognition systems with OpenCV and Python.

Skills you'll gain

Details to know

Shareable certificate

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Build your subject-matter expertise

This course is part of the Apply OpenCV for Real-Time Computer Vision Projects Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

By completing this course, learners will be able to explain core computer vision concepts, apply edge detection techniques, build facial image datasets, train face recognition classifiers, and develop real-time face and eye recognition systems using OpenCV and Python.

This course provides a step-by-step, hands-on approach to face recognition, starting from foundational image processing concepts and progressing to a fully working real-time recognition system. Learners gain practical experience with edge detection algorithms such as Canny, learn how to collect and organize facial datasets, and understand how classifiers are trained and evaluated for recognition tasks. What makes this course unique is its project-driven structure, where every concept directly contributes to building a real application. Instead of isolated theory, learners see how preprocessing, detection, training, and recognition fit together in a complete pipeline. The course is ideal for beginners in computer vision as well as developers who want to implement, analyze, and deploy face recognition solutions using OpenCV. By the end of the course, learners will have the confidence and skills to build their own face recognition projects and extend them to real-world applications.

Module details

This module introduces learners to the fundamentals of computer vision using OpenCV, focusing on edge detection techniques and the creation of a structured facial image dataset required for building face recognition systems.

What's included

6 videos3 assignments

6 videosTotal 56 minutes

Introduction of Project6 minutes
Edge Detection1 minute
Canny Edge Detection 9 minutes
Canny Edge Detection Continue13 minutes
Creating Dataset13 minutes
Creating Dataset Continue13 minutes

3 assignmentsTotal 50 minutes

Graded - Foundations of Face Recognition with OpenCV30 minutes
Getting Started with Computer Vision Basics10 minutes
Advanced Edge Detection & Dataset Creation10 minutes

This module guides learners through training face recognition classifiers and deploying a real-time system that detects and recognizes faces and eyes using OpenCV.