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 to face landmarking ##

The faithful identification of facial features and landmarks constitutes a rudimentary process and a foundation for a wide range of more advanced computer vision tasks, such as facial recognition and facial expression analysis.

At their highest level, the facial landmarks are regarded as a set of key points, generally located on the corners of the main facial components. By leveraging computer vision techniques, the detection of face landmarks can be used for a lot of applications, including the following:

* Extraction of face parts (that is, nose, eyes, mouth, jawline, and so on)
* Alignment of faces
* Swapping of faces
* Detection of blinking eyes
* Detection of drowsiness


# Objective ##

This lesson will present  the Dlib Python library and will help to visualize 68 face key points. The inner algorithms of this robust library will allow us to detect several features of the human face.
The facial landmark detector of this library will return a map composed of 68 points known as landmark points that surround each of the face features, as seen below:


Let’s learn to extract face landmarks using the Dlib Python library.


Introduction

Core Functions

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Face Landmarking Using Dlib

Introduction to face landmarking

Objective