updated_FA.tar.gz

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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

## About ##

Virtual makeovers are one of the hottest fields in computer vision, machine learning, and augmented reality. This game-changing technology creates personalized shopping experiences and fosters the mainstreaming of AR technology. Virtual makeovers helped many beauty brands during the COVID-19 pandemic and beyond.

In short, virtual makeover applications allow makeup shoppers to try on a range of products and styles remotely without wasting beauty products or spending time cleaning up later. With the help of their phone’s front-facing camera, customers can now experiment with thousands of shades and textures of lipstick, eyeshadow, and other cosmetics by applying them instantly to their faces.

This technology works by laying a mask over specific key points on our face in order to implement the desired effect while taking into account the subtleties of each face and its distinctive landmarks.


# About ##

Virtual makeovers are one of the hottest fields in computer vision, machine learning, and augmented reality. This game-changing technology creates personalized shopping experiences and fosters the mainstreaming of AR technology. Virtual makeovers helped many beauty brands during the COVID-19 pandemic and beyond.

In short, virtual makeover applications allow makeup shoppers to try on a range of products and styles remotely without wasting beauty products or spending time cleaning up later. With the help of their phone’s front-facing camera, customers can now experiment with thousands of shades and textures of lipstick, eyeshadow, and other cosmetics by applying them instantly to their faces.

This technology works by laying a mask over specific key points on our face in order to implement the desired effect while taking into account the subtleties of each face and its distinctive landmarks.


Introduction

Introduction

Core Functions

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Introduction

About