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

Which technique is used to conceal faces in digital images?

Which technique is used to conceal faces in digital images?

Reducing the number of distinct colors in the resulting image.

Increasing the number of distinct colors in the resulting image.

Converting the facial image to grayscale format and then automatically colorizing it using a CNN model.

Which of the following describes the correct method for adding cartoon effects to a face image?

Which of the following describes the correct method for adding cartoon effects to a face image?

Analyzing specific regions close to the eyes and detecting frame edges.

Implementing a CNN network for glasses detection. The CNN was trained using a set of facial images with and without glasses.

Finding glasses contours by a Canny edge detection around the eye and glasses area.

Segmenting the frames of the glasses based on the color distinction between the skin and the glasses.

**(Select all that apply.)** Which of the following procedures were used to detect glasses?

(Select all that apply.) Which of the following procedures were used to detect glasses?

The Delaunay triangulation of the face landmarks.

The localization of the center of the face.

Which step is considered a prerequisite for the face-swapping or facial-components swapping process?

Which step is considered a prerequisite for the face-swapping or facial-components swapping process?

Masking the face features not associated with skin area, like the inner mouth or right eyebrow.

Extracting image pixels that correspond to the skin color space.

**(Select all that apply.)** Which of the following steps are required to segment the skin area in a facial image?

(Select all that apply.) Which of the following steps are required to segment the skin area in a facial image?

**(Select all that apply.)** Which of the following measurements are considered in the face shape detection process?

(Select all that apply.) Which of the following measurements are considered in the face shape detection process?

Which color space is used in the automatic colorization process?

Which color space is used in the automatic colorization process?

AR augments real-world surroundings by adding digital elements to a live view, often by using the camera on a smartphone.

**(Select all that apply.)** Which of the following statements are true about AR and VR?

(Select all that apply.) Which of the following statements are true about AR and VR?

**(Select all that apply.)** What are the applicable domains of face aging?

(Select all that apply.) What are the applicable domains of face aging?

Introduction

Core Functions

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Quiz: Manipulation Functions