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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

Face recognition identifies human faces based on visual appearance. Facial recognition systems mostly make their decisions based on distance measures. Distance metrics play a key role in matching images.

## What is the Manhattan distance metric?

In short, the **Manhattan distance**, also known as city block distance or L1 distance, represents the distance necessary to get from one data point to another. The Manhattan distance between two points represents the sum of the absolute differences between their Cartesian coordinates.

The following figure is a visual illustration of the Manhattan distance metric:


# Introduction

Face recognition identifies human faces based on visual appearance. Facial recognition systems mostly make their decisions based on distance measures. Distance metrics play a key role in matching images.

# What is the Manhattan distance metric?

In short, the **Manhattan distance**, also known as city block distance or L1 distance, represents the distance necessary to get from one data point to another. The Manhattan distance between two points represents the sum of the absolute differences between their Cartesian coordinates.

The following figure is a visual illustration of the Manhattan distance metric:


Let’s match face signatures using the Manhattan distance algorithm.

Face Matching Using the Manhattan Distance Algorithm

Introduction

Core Functions

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Face Matching Using the Manhattan Distance Algorithm

Introduction

What is the Manhattan distance metric?