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

A digitally recorded representation of a person's face.

A digital photograph of a person’s face, including the common characteristics of his facial structure.

A type of biometric data that electronically identifies an individual.

A basis for comparison with data already captured from faces.

**(Select all that apply.)** What is a faceprint?

(Select all that apply.) What is a faceprint?

Image acquisition &rarr; Face detection &rarr; Face capture and analysis &rarr; Face match

Image acquisition &rarr; Face capture and analysis &rarr; Face detection &rarr; Face match

Image acquisition &rarr; Face detection &rarr; Face match &rarr; Face capture and analysis

Image acquisition &rarr; Face detection &rarr; Face features extraction &rarr; Face match

What is the correct order of steps a facial recognition system performs?

What is the correct order of steps a facial recognition system performs?

Face authentication is a one-to-many match.

Face authentication is a one-to-one match.

**(Select all that apply.)** Which of the following concepts is valid?

(Select all that apply.) Which of the following concepts is valid?

The generated face encodings for two people with different faces would be equal.

The generated face encodings for two people with different faces would be different.

The distance computed between two encodings of the same person is low.

The Euclidean and Manhattan algorithms return the same values when calculating the distance between two faces encodings.

**(Select all that apply.)** Which options are correct?

(Select all that apply.) Which options are correct?

The Euclidean distance represents the shortest distance between two points.

The Euclidean distance represents the distance that would be traveled to get from one data point to another.

The Euclidean distance between two points represents the length of a line segment between those two points.

The Euclidean distance is always greater than the Manhattan distance.

What is the Euclidean distance metric?

Evaluate your understanding of facial recognition.

Introduction

Core Functions

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Quiz: Face Recognition