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Analyzing and Transforming Faces in Python
Perform facial recognition with Python libraries MediaPipe, Dlib, and DeepFace. Explore face detection, analytics, and transformation effects, gaining crucial biometric software skills.
5.0
50 Lessons
15h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- A strong understanding of the basics of facial recognition
- A working knowledge of three unique machine learning libraries: MediaPipe, Dlib, and DeepFace
- A deep familiarity with common facial analysis techniques using Python
- The ability to use deep neural networks to identify age, gender, race, and emotion from facial expressions
- The ability to apply various artistic effects to faces
- A complete perspective on facial recognition models and the tools to build a multifaceted model to common facial analysis tasks
- Hands-on experience with Python, MediaPipe, Dlib, and DeepFace for facial analysis
Learning Roadmap
1.
Introduction
Introduction
Get familiar with Python-based face analysis technologies, libraries, and their real-world applications.
2.
Core Functions
Core Functions
Unpack the core of face detection, landmarking, triangulation, and alignment techniques in Python.
3.
Predictive Analytics
Predictive Analytics
6 Lessons
6 Lessons
Examine age, gender, emotion, race, and beauty prediction using facial images in Python.
4.
Manipulation Functions
Manipulation Functions
19 Lessons
19 Lessons
Grasp the fundamentals of face manipulation functions using Python for enhanced image analysis.
5.
Virtual Makeover Functions
Virtual Makeover Functions
7 Lessons
7 Lessons
Dig deeper into the implementation of Python functions for virtual makeup application in images.
6.
Face Recognition
Face Recognition
5 Lessons
5 Lessons
Follow the process of facial recognition using encodings and distance algorithms in Python.
8.
Appendices
Appendices
2 Lessons
2 Lessons
Break down essential Python libraries and setup steps for face analysis projects.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
Face analysis technology is a rapidly growing biometric software discipline with wide-ranging applications in surveillance, forensics, game design, and social media. As with other machine learning domains, Python has several libraries for computer vision, image analysis, and pattern recognition that make it ideal for facial analysis.
This course is a hands-on introduction to facial recognition with three unique libraries—MediaPipe, Dlib, and DeepFace. You’ll start with face detection, landmarking, and face alignment before exploring common analytics like age, gender, and emotional prediction based on facial expressions. Next, you’ll identify common facial features before transforming them by adding blurring, sketching, and cartoon effects or swapping color palettes. You’ll finish by performing full makeover functions, manipulating cheeks, lips, eyes, and brows.
By the end of this course, you’ll have a strong foundation in popular facial recognition and manipulation libraries in Python.
ABOUT THE AUTHOR
Bassem Marji
Project implementation manager with a proven track record of success.
Trusted by 2.9 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
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