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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setup

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 ##

Some social media apps like Snapchat have gained unprecedented popularity because of their unique and creative features. One of these innovative features is face filters. Nowadays, more and more teenagers are extensively using face filters for fun, and to make their pictures look different.

This face filter technique, continuously evolving, makes use of artificial intelligence and computer vision to detect a human face and its underlying features. Then, the AI can overlay a gallery of filters on the face—an entertaining and amusing gimmick. 


# Objective ##

This lesson will walk you through how to add a face filter to a preexisting photo. It will also illustrate the steps needed to develop a lightweight Python utility that allows us to drastically distort the faces in a digital image by overlaying the face of the Joker (a DC comics villain) over the localized faces.

# Dependencies ##

We’ll be using the following Python libraries:

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# Let’s code the utility

Let’s take a look at the core functions of this utility.

## The `read_filter_img` function

This function is used to load the face filter image and collect the coordinates of its respective landmark points. Let’s go through this function.

* **Line 5** loads the face filter image specified by the preset global parameter `FILTER_IMG`.
* **Line 6** collects the image dimensions.

* **Line 7** converts the image from the BGR to the RGB format. It’s worth noting that OpenCV loads images in BGR format.

* **Line 10** grabs the landmarks relative to the face filter image with the help of the `MediaPipe FaceMesh` sub-module.

* **Lines 13–16** gather the landmarks’ coordinates.
The complete code of the function `read_filter_img` is shown below:

Let’s learn how to apply a face filter on a digital face image.

Draw the Face Filter

Introduction

Core Functions

Face Detection Using Dlib and DNN in OpenCV

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Drawing Face Filter