Perform facial recognition with Python libraries MediaPipe, Dlib, and DeepFace. Explore face detection, analytics, and transformation effects, 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

# About ##

Whether you’re seeking to deepen your knowledge of the face analysis ecosystem or get a comprehensive overview of face analysis technologies, this course is your best resource.

This course is intended for Python developers at all levels of expertise, who want to gain insight into the broad spectrum of face analysis domains. To get the most out of this course, you must be fairly comfortable coding in Python.

Python is considered one of the world’s fastest-growing programming languages as several popularity indexes like **TIOBE** have noted.

The Popularity of Programming Languages **PYPL** Index also shows that the Python programming language is more widely used than other languages as seen in the chart below:

Adopted and developed since the 1980s, Python is the blueprint for many programming tasks. The Python programming language is considered somewhat of a breakthrough because it allows developers to leverage the power of its underlying, well-supported libraries for image processing and computer vision.

By the end of this course, you'll master the tools needed for a variety of facial applications ranging from face detection to face manipulation. There will be a strong focus not only on the foundational theories and the underlying concepts but also on practical applications and implementations. 

In fact, instead of explaining the basis and the theories, we help you to build, in a constructive manner. By adopting a scaffolding approach, this course will help you create a solid coding framework for tackling a wide range of facial applications using Python, with a strong emphasis on hands-on implementation in real-world scenarios through a series of dedicated lab sessions.

# What you'll learn ##

This course is divided into six main modules covering various general concepts, to practical applications. Designed to be completed within seven to eight weeks, this course will accomplish the following:

* Introduce you to key pillars of facial applications and walk you through the main Python libraries dedicated to those applications.

* Concentrate on basic processing functions that embody several tasks like face detection, facial landmarks localization, and the Delaunay triangulation of face landmarks.

* Cover deep learning algorithms for predictive face analytics that factor in age, gender, emotions, and race predictions.

* Provide you with guidance on a range of face manipulation functionalities from face blurring to face swapping.

* Explore a wide array of virtual makeover functions so that you can experiment with several auto-beautification features.

* Build a complete model for face recognition featuring several approaches that can compute the distance between facial feature points.

All of these concepts will be discussed later in much greater detail. Each chapter of this course includes a small, well-vetted quiz designed to test your knowledge and reinforce key information.

# Expected outcomes ##

The prime objective of this course is to help you learn and implement new face analysis technologies and concepts. It will equip you with enough programming experience to start developing tools and utilities, tailored to your needs that can be used for face analysis and manipulation. 

Get an introduction to face analysis through Python.

Course Overview

Introduction

Core Functions

Face Detection Using Dlib and DNN in OpenCV

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Course Overview