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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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

# Abstract ##

Our face is considered one of the most significant identification features. With the continuous advancements in computer vision and artificial intelligence, biometric technologies have exploded and gained unprecedented popularity. Facial recognition is one of those sparks that have created game-changing technologies that have turned out to be pervasive.

Facial recognition systems allow a fast and secure way of identifying individuals since a face is undeniably connected to its owner in contrast to access codes or pin codes that attackers could guess.


# Factors driving face-recognition adoption

Facial recognition systems have been shaping the landscape in recent years and have been integrated into numerous industries. The main factors propelling the growth of facial recognition across industry verticals are listed below:

* Ease of use in the existing hardware infrastructure, for example, the availability of cameras.

* Ease of deployment and implementation compared to other touchless systems.

* Contactless technology that doesn’t involve physical interaction with the sensor, preventing the spread of disease.

* Near-perfect accuracy, better security, and a high level of reliability.

* The rise of the surveillance industry and increasing investment in facial recognition technologies, mainly by governments and the defense sectors.


# How face recognition works

Facial recognition systems analyze the face structure and its uniquely distinguishable features on a micro-scale and then transform them into a unique signature. Some of the facial features that are analyzed are listed below:

* Distance between the eyes
* Jawline shape
* Cheekbone shape
* Nose size
* Mouth position

The signature generated known as a **faceprint** is a unique, digitally recorded representation of a person’s face. The faceprint is compared to the signatures stored within an existing database or a watch list of known faces in order to do the following:

* Verify a given person’s identity, such as granting the owner access in mobile phones.

* Identify a given person by running a comparison process and returning a list of top-ranked matches.

Typically a facial recognition system performs the following functions:

* **Image acquisition** using a camera capture.

* **Face detection** by scanning a digital image to locate human faces.

* **Face capture and analysis** converts the facial distinctive features into a faceprint and transforms the analog information of the face into a set of digital information (faceprint) based on the facial features.

* **Face match** to verify whether the faceprint matches the ones stored in a database or a watch list of known faces.

The accuracy of a facial recognition system largely depends on how well the underlying deep learning models are trained, as well as the size and quality of the datasets of digital images that are being used to train these models.

A gentle introduction to face recognition.

Introduction

Introduction

Core Functions

Face Detection Using Dlib and DNN in OpenCV

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Introduction

Abstract

Factors driving face-recognition adoption