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.)** What are the main applications of face detection?

(Select all that apply.) What are the main applications of face detection?

**(Select all that apply.)** Which Python libraries help extract face landmarks?

(Select all that apply.) Which Python libraries help extract face landmarks?

Which Python library is equipped with a facial landmark detector that allows the extraction of a larger number of face key points?

Which Python library is equipped with a facial landmark detector that allows the extraction of a larger number of face key points?

Creating a mesh of adjacent, nonoverlapping triangles from a predefined dataset of distinct points.

Subdividing an area into triangles where the circumcircle of each triangle contains only the vertices of the triangle.

Identifying the edges of the area considered.

Changing the structure of the area considered.

**(Select all that apply.)** What are the objectives of the Delaunay triangulation technique?

(Select all that apply.) What are the objectives of the Delaunay triangulation technique?

Face alignment decreases the accuracy of the face recognition process.

Face alignment is seen as a normalization technique.

Face alignment computes the angle between the face and eyes.

Face alignment is a fundamental task for facial analysis applications.

**(Select all that apply.)** Which of the following options is correct?

(Select all that apply.) Which of the following options is correct?

Assess your understanding of the core functions.

Introduction

Core Functions

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Quiz: Core Functions