Delve into facial recognition with Python libraries MediaPipe, Dlib, and DeepFace. Explore face detection, analytics, transformation effects, and full makeovers, gaining crucial biometric software skills.

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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

Which of these languages is the world’s fastest-growing programming language?


Which of these languages is the world’s fastest-growing programming language?

**(Select all that apply.)** What are the main face-related technologies?

(Select all that apply.) What are the main face-related technologies?

Localize human faces within digital images.

Identify and name human faces in digital images.


**(Select all that apply.)** What is the main purpose of face detection technology?

(Select all that apply.) What is the main purpose of face detection technology?

It’s faster than other compiled languages such as C or Java.


It’s portable across different platforms and environments.


It leverages well-integrated libraries for machine learning.


**(Select all that apply.)** Why is Python the preferred choice for computer vision projects?

(Select all that apply.) Why is Python the preferred choice for computer vision projects?

To enable computers to process or transform the spotted images.


To allow computers to see and understand visual data.

To automate tasks that the human visual system can do.


To enhance the images through several processes like noise reduction, contrast enhancement, and so on.


**(Select all that apply.)** What is the aim of computer vision?

(Select all that apply.) What is the aim of computer vision?

Assess your knowledge of face-related technologies.

Introduction

Core Functions

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Quiz: Face Technology Basics