Delve into facial recognition with Python libraries MediaPipe, Dlib, and DeepFace. Explore face detection, analytics, transformation effects, and full makeovers, 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 ##

The lips are an important defining feature of the face, making them an essential aspect of facial recognition. In addition, lips can be very important when predicting and estimating emotions using facial analysis software. Outside of face analysis, they can also serve as an indicator of general health. For example, when lips become bluish, this could be an indication of a lack of oxygen in the blood.


# Objective ##

This lesson will explain the steps required for discerning the color and shape of a person’s lips from their facial image in an automated manner. 

Below, we can see the series of steps that shape this process:



# Dependencies ##

We’ll be using the following external Python libraries.


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# Coding the utility ##

Let’s take a look at the core functions of this utility.

## The `process_lips_area` function ###

This function segments the lips and extracts the top dominant color from this area. The function allows us to perform the following steps. 

* **Lines 7–8** convert the mouth’s feature points into a `Numpy` array.
* **Lines 11–12** crop the lips, which is the region of interest, from the image. 
* **Line 15** creates a black mask for the selected image.
* **Line 16** draws a contour, filled with a white color, comprising the mouth’s feature points.
* **Line 17** crops the white area that comprises the mouth’s feature points, and considers this as a white mask. 
* **Line 20** using the `OpenCV` function bitwise_and, we’ll extract the lips’ region that is colored. In brief, this function works as follows:
  * ROI of any color + white mask = masked area with colors
  * ROI of any color + black mask = masked area in black color

* **Line 22** gathers five dominant colors from the extracted lips area by invoking the function `extract_dominant_colors`.
* **Line 23** calls the function `get_top_dominant_color` to extract the top dominant color out of the list of dominant colors previously collected.

The complete code of the function `process_lips_area` is shown below:

Let’s learn how to recognize the color and shape of the lips from a face image.


Library	Version
Dlib	19.17.0
opencv-python	4.4.0.46
scikit-learn	1.0.1
NumPy	1.19.4
webcolors	1.11.1
filetype	1.0.7

Introduction

Core Functions

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Recognizing Lips Color

Introduction

Objective

Dependencies

Coding the