This one-stop course provides students with well-rounded guidance at the forefront of face analysis, manipulation, and recognition technologies using Python.

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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

**(Select all that apply.)** Which attributes can be estimated from facial analysis?

(Select all that apply.) Which attributes can be estimated from facial analysis?

Which Python library did we utilize for age prediction?

Which Python library did we utilize for age prediction?

What type of convolutional neural network (CNN) loads using the OpenCV library for gender estimation?


What type of convolutional neural network (CNN) loads using the OpenCV library for gender estimation?

A technology targeted to analyze the sentiments conveyed through a face image.

A technology that estimates a person’s race from a facial image.


A technology in computer vision and artificial intelligence domains, owing to its remarkable academic and commercial potential.

A technology consisting of multiple steps—namely, face detection, feature extraction, and expression classification.


**(Select all that apply)** What is Facial Emotion Recognition (FER)?

(Select all that apply) What is Facial Emotion Recognition (FER)?

Multiple estimations with different scores.

The race with the highest estimated score.

The race with the lowest estimated score.

What is the expected output of running the race prediction function? 

What is the expected output of running the race prediction function?

Evaluate your knowledge of the predictive analytics functionalities.

Introduction

Core Functions

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Quiz: Predictive Analytics