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

# Introduction ##

For decades, decoding the emotional expressions of a face has been an interesting topic for research in the field of psychology and, more recently, in the field of human-computer interaction (HCI) because of its significant commercial potential.

Facial expressions are an intuitive reflection of a person’s mental state and a common form of non-verbal communication. Most people can effectively express their personal feelings and communicate their intentions through facial expressions.


# What is Facial Emotion Recognition (FER)? ##

**Facial Emotional Recognition (FER)** is mainly a technology designed to analyze the sentiments captured by different forms of media, such as pictures and videos. Built on top of AI, FER belongs to the affective computing family of technologies. It detects and analyzes different facial expressions in order to conclude what emotions a person is showing.

The table below depicts the top emotions along with their respective and most common facial expressions:


Most common expressions

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# What are the applications of FER? ##

FER usage may cover an extensive variety of applications, including the following:

* Analyzing customer behavior for advertising purposes—for example, analyzing customers’ emotions while shopping.

* Detecting driver fatigue.

* Assessing audience engagement so that the emotional reaction of users reading learning content can be detected and the learning path can be adapted accordingly.


* Monitoring employee moods.


* Testing video games.


Let’s learn to extract emotions from a face image.


Introduction

Core Functions

Face Detection Using Dlib and DNN in OpenCV

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Emotions Prediction