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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

## Introduction
Along with the wisdom and achievements that come with getting older, there are inevitable changes in our physical appearance. Typically, aging affects our facial appearance.

**Face aging**, also known as age synthesis or age progression, revolves around rendering a face in a way that forecasts its future appearance. This process is of great importance for various domains, like those listed below:

* **Security fields:** Age-related changes are important to information forensics.

* **Cross-age face recognition systems:** Age-indicative information can be used to increase face-recognition accuracy.

* **Entertainment-related applications:** FaceApp is one of the best mobile applications for aging and de-aging facial images.

## Objective
This lesson will help us develop a lightweight Python utility capable of predicting how a person will look after growing old using their facial image.

# Introduction
Along with the wisdom and achievements that come with getting older, there are inevitable changes in our physical appearance. Typically, aging affects our facial appearance.

**Face aging**, also known as age synthesis or age progression, revolves around rendering a face in a way that forecasts its future appearance. This process is of great importance for various domains, like those listed below:

* **Security fields:** Age-related changes are important to information forensics.

* **Cross-age face recognition systems:** Age-indicative information can be used to increase face-recognition accuracy.

* **Entertainment-related applications:** FaceApp is one of the best mobile applications for aging and de-aging facial images.

# Objective
This lesson will help us develop a lightweight Python utility capable of predicting how a person will look after growing old using their facial image.

Let’s learn how to age the detected faces in a digital image.

Age Faces

Introduction

Core Functions

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Age Faces

Introduction

Objective