Computer Vision: Face Recognition Quick Starter in Python
Learn face recognition in Python using OpenCV and dlib. Master face detection, expression analysis, and real-time implementation.
Learn face recognition in Python using OpenCV and dlib. Master face detection, expression analysis, and real-time implementation.
This comprehensive course teaches face recognition implementation using Python, from basic concepts to advanced applications. Students learn to work with essential libraries like OpenCV and dlib, implementing face detection, recognition, and analysis systems. The curriculum covers real-time face detection, expression analysis, age and gender classification, and face landmark visualization. Through hands-on projects, learners develop practical skills in computer vision applications.
Instructors:
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What you'll learn
Implement face detection and recognition using Python
Master OpenCV and dlib libraries for computer vision
Develop real-time face detection applications
Create facial expression analysis systems
Implement age and gender classification
Work with face landmarks for advanced applications
Skills you'll gain
This course includes:
269 Minutes PreRecorded video
9 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 26 modules in this course
This course provides a comprehensive introduction to face recognition technology using Python. Students learn to implement various aspects of face recognition, from basic detection to advanced features like expression analysis and real-time processing. The curriculum covers essential libraries and tools including OpenCV and dlib, with practical applications in facial expression detection, age and gender classification, and face landmark visualization. Through hands-on exercises, learners develop skills in both static and real-time face recognition implementations.
Introduction
Module 1 · 20 Minutes to complete
Environment Setup: Using Anaconda Package
Module 2 · 7 Minutes to complete
Python Basics
Module 3 · 33 Minutes to complete
Setting Up Environment - Additional Dependencies (with DLib Fixes)
Module 4 · 23 Minutes to complete
Introduction to Face Detectors
Module 5 · 18 Minutes to complete
Face Detection Implementation
Module 6 · 12 Minutes to complete
cv2.imshow() Not Responding Issue Fix
Module 7 · 1 Minutes to complete
Real-Time Face Detection from Webcam
Module 8 · 32 Minutes to complete
Video Face Detection
Module 9 · 2 Minutes to complete
Real-Time Face Detection - Face Blurring
Module 10 · 4 Minutes to complete
Real-Time Facial Expression Detection - Installing Libraries
Module 11 · 22 Minutes to complete
Real-Time Facial Expression Detection - Implementation
Module 12 · 15 Minutes to complete
Video Facial Expression Detection
Module 13 · 1 Minutes to complete
Image Facial Expression Detection
Module 14 · 20 Minutes to complete
Real-Time Age and Gender Detection Introduction
Module 15 · 5 Minutes to complete
Real-Time Age and Gender Detection Implementation
Module 16 · 12 Minutes to complete
Image Age and Gender Detection Implementation
Module 17 · 18 Minutes to complete
Introduction to Face Recognition
Module 18 · 4 Minutes to complete
Face Recognition Implementation
Module 19 · 21 Minutes to complete
Real-Time Face Recognition
Module 20 · 26 Minutes to complete
Video Face Recognition
Module 21 · 3 Minutes to complete
Face Distance
Module 22 · 11 Minutes to complete
Face Landmarks Visualization
Module 23 · 27 Minutes to complete
Multi Face Landmarks
Module 24 · 11 Minutes to complete
Face Makeup Using Face Landmarks
Module 25 · 7 Minutes to complete
Real-Time Face Makeup
Module 26 · 1 Hours to complete
Fee Structure
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