This one-stop course provides students with well-rounded guidance at the forefront of face analysis, manipulation, and recognition technologies using Python.

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Face Detection

Face Landmarking Using Dlib

Face Landmarking Using MediaPipe

Delaunay Triangulation of Facial Landmarks

Face Alignment

Age Prediction

Gender Prediction

Emotions Prediction

Race Prediction

Recognizing Eyes Color

Calculating Eyes Open Ratio

Detecting Eyeglasses

Recognizing Lips Color

Changing Lips Color

Detecting Skin Color

Swapping Faces

Swapping Eyes

Drawing Face Filter

Blurring Face

Cartooning Face

Face Encoding

Face Matching - Manhattan

Face Matching - Euclidean

Try Virtual Glasses

Change Background

Expand Face Features

Remove Background

Predict Beauty

Detect Face Shape

Condense Eyebrows

Drawing Eyeliners

Applying Blush

Detect Hair Color

Change Hair Color

Aging Faces

Auto-colorize Facial Photos

Calculate Face Golden Ratios

Add AR effects To Facial Photos

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.

Analyzing and Transforming Faces in Python

**(Select all that apply.)** What are the user's major concerns when dealing with virtual makeover applications?

(Select all that apply.) What are the user’s major concerns when dealing with virtual makeover applications?

Which virtual makeover feature uses an interpolation function?

Which virtual makeover feature uses an interpolation function?

By overlaying a white mask on top of the blush area.

How did we emphasize the blush area?

**(Select all that apply.)** Which of the following steps are part of the process to change the lip color?

(Select all that apply.) Which of the following steps are part of the process to change the lip color?

Give users controversial messages about attractiveness, and blur the line between reality and fantasy.

**(Select all that apply.)** What are the main benefits of virtual makeover applications?

(Select all that apply.) What are the main benefits of virtual makeover applications?

Introduction

Core Functions

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Quiz: Virtual Makeover Functions