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

# Introduction ##

Most people looking lipstick hope to find a shade that makes them more attractive by enhancing their natural color. If you’re struggling to find a suitable lipstick color, look no further and stay focused on this lesson in order to find the perfect matching shade!

# Objective

This lesson will demonstrate the steps required for virtually trying out a different lipstick color for the detected faces within a digital image.

This process involves the following steps:



# Dependencies ##

We’ll be using the following external Python libraries.

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# Let’s code the utility!

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

## The `change_lipstick_color` function
This function is used to apply a new color to the pixels that constitute the mouth region. Let’s define this function.

* **Lines 7–8** loop over the points of the mouth landmark and add them to a list named `points`.

* **Line 10** converts the generated list to a 2D `Numpy` array.

* **Line 12** draws a polygon on the points selected using the `OpenCV` function `fillPoly`. This polygon will be filled with the targeted color specified as a global preset parameter called `TARGET_COLOR`.

The complete code of the function `change_lipstick_color` is shown below:

Let’s learn how to change the color of the lips in a digital image.

Change Lip Color

Library	Version
`Dlib`	19.17.0
`opencv-python`	4.4.0.46
`imutils`	0.5.3
`numpy`	1.19.4
`filetype`	1.0.7

Introduction

Core Functions

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Change Lip Color

Introduction

Objective

Dependencies

Let’s