Perform facial recognition with Python libraries MediaPipe, Dlib, and DeepFace. Explore face detection, analytics, and transformation effects, gaining crucial biometric software skills.

updated_FA.tar.gz

setup

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

Throughout this course, you came across many Python libraries for face-related applications. You’ve already managed the installation of these libraries and other dependencies on the platform. If you want to create a similar setup locally on your computer, you may follow the steps listed below. It would be helpful get a	quainted with basic Python administrative tasks, such as creating and activating a Python virtual environment. Please also note that the following installation guide is for Linux-based computers.

# The application skeleton ##

First and foremost, you need to make sure that Python3 is installed on your system. 

* Create a folder for hosting the project called `face_analyzer`.

* Create a virtual environment and activate it.

* Create a file named `requirements.txt` and add to it the libraries pertinent to this project.
You can copy the following content to install all the libraries:

```
itsdangerous==2.0.1
Flask==1.1.2
filetype==1.0.7
numpy==1.19.4
opencv_python==4.4.0.46
opencv_contrib_python==4.5.3.56
mediapipe==0.8.9
tensorflow==2.4.1
pandas==1.1.4
gdown==3.10.1
fer==21.0.3
webcolors==1.11.1
scikit-learn==1.0.1
retina_face==0.0.5
deepface==0.0.68
imutils==0.5.3
face-recognition-models==0.3.0
face-recognition==1.3.0
waitress==2.0.0
onnx==1.10.2
onnxruntime==1.9.0
```

* Install the libraries to the project using the following snippet:

```shell
pip install –r requirements.txt
```

 Find out how to install this course's dependencies for your local setup.

Appendix B - Setup of Dependencies

Find out how to install this course's dependencies for your local setup.

Appendix B: Setup of Dependencies

Introduction

Core Functions

Face Detection Using Dlib and DNN in OpenCV

Predictive Analytics

Manipulation Functions

Virtual Makeover Functions

Face Recognition

Conclusion

Appendices

Appendix B: Setup of Dependencies