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.
Face analysis technology is a rapidly growing biometric software discipline with wide-ranging applications in surveillance, fore...Show More
WHAT YOU'LL LEARN
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
A strong understanding of the basics of facial recognition
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TAKEAWAY SKILLS
Content
1.
Introduction
4 Lessons
Get familiar with Python-based face analysis technologies, libraries, and their real-world applications.
2.
Core Functions
6 Lessons
Unpack the core of face detection, landmarking, triangulation, and alignment techniques in Python.
3.
Predictive Analytics
6 Lessons
Examine age, gender, emotion, race, and beauty prediction using facial images in Python.
4.
Manipulation Functions
19 Lessons
Grasp the fundamentals of face manipulation functions using Python for enhanced image analysis.
5.
Virtual Makeover Functions
7 Lessons
Dig deeper into the implementation of Python functions for virtual makeup application in images.
6.
Face Recognition
5 Lessons
Follow the process of facial recognition using encodings and distance algorithms in Python.
8.
Appendices
2 Lessons
Break down essential Python libraries and setup steps for face analysis projects.
Certificate of Completion
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Course Author:
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