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

# A retrospective on this course
Drawing from the course lessons and looking back over the course objectives, you should now be able to:

* Distinguish between different face-related technologies.

* Explain the processes involved in facial analysis and facial recognition.

* Instead of using online services and off-the-shelf programs for manipulating facial images, you can exploit the expanded utilities to enhance productivity and increase privacy.

* Develop reliable end-to-end solutions for virtual face makeovers or facial recognition.

* Get acquainted with cutting-edge machine learning algorithms that can get age, gender, emotion, and race estimations from a facial image.

The utilities covered in this course are readily available for production rollout. We hope that it will increase your overall productivity and end-product quality.


# Course reflections

The experience you’ve gained from this course will undoubtedly sharpen your skills and give you a leg up on these new and advanced technologies. You’re now well acquainted with the leading tools that you’ll need to develop full-blown face-related applications using the Python programming language. You’re now well prepared to transform the knowledge you accumulated throughout this journey into effective actions.

Wrap up and recapitulate the course goals.

Final Thoughts

Introduction

Core Functions

Face Detection Using Dlib and DNN in OpenCV

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Final Thoughts

A retrospective on this course