Face Detection Tutorial

Hey, This is single page where you can understand how face recognition can be done. Go through the post one by one. Many are working on the computer's vision power. We are well aware in today's era voice recognition is already there in other words now our computer can hear us. By the help of NLTK the hearing power and understanding the word is much improved.

1)   Setting up Face Recognition Basic requirements

By this, we are now focusing how better we can make our computer or any other machine vision improved. I got curious about this and going to invest my time in this with doing a full-time job so the process will be slow.

For start, I select Python language as it is easy to understand and there are a lot of packages available. Preparing a Linux PC with Python 3X with OpenCV and its dependency may take 2 - 3 hour depending upon your machine.

Note: Consider having PC with at least 4gb RAM, not because it is Python won't support less RAM but the thing we are going to do may need RAM, plus Python is a scripting language it is slow as compare to other but efficient and easy.

Let's start:

Requirements:

1) Python3X
2) OpenCV

Python3X

On Ubuntu machine, Python3 will be preinstalled as there are many OS files which are dependent on Python3X as well as Python2X. Saying this please don't try to remove any of the Python version and be sure the package you try to use should support.

1) How to check the Python version:
     Open terminal (ctrl+atl+T)
     Type "python3" hit enter
     Type "exit()" to return to prompt

** As per today I have 3.6.5 Python version

2) How to install pip:
     Type "sudo apt-get install python3-pip" give Admin password and hit enter

OpenCV

OpenCV-Python can be installed in Ubuntu in two ways:

  1) Install from pre-built binaries available in Ubuntu repositories
  2) Compile from the source.

Installation procedure 

Congratulations! if this is done.

2) Installing Dlib Library

Once the machine setup is done successfully as described in my previous post ("Click me!") we are good to go to install one more dependency which Dlib. Posting a completely new post just for Dlib because I struggled and like almost I just saved my OS from crashing so it deserves.

Installing with the correct command is all that is needed there are many tutorials available for this installation and more I refer more I got confused so after 3 to 4 hour of struggle finally I came up with the necessary command which will be enough to install Dlib on your machine and we can proceed further to install the face_recognition package.

Below are the commands I used and work perfectly:

·       sudo apt-get install build-essential cmake

·       sudo apt-get install libgtk-3-dev

·       sudo apt-get install libboost-all-dev wget        https://bootstrap.pypa.io/get-pip.py

·       sudo python3 get-pip.py

·       sudo pip3 install numpy

·       sudo pip3 install scipy

·       sudo pip3 install scikit-image

·       sudo pip3 install dlib 

·       sudo pip3 install face_recognition

All the best! :)

3)  Introduction of Face Detection

We all know that face recognition is possible and we came across it on daily basis nowadays, for example, your front camera. some phones draw a circle around your face because the code did recognize a human face.

But how but it recognize your face in real-time. yes, it is possible many security software use it and I am just going to demonstrate too. In this post, I am going to show you and in the upcoming post, I'll share how can you code it for yourself. trust me it is easy to do.

Have fun!

4) Code your own Face Detection

As shown in the previous post we can code to detect a particular face from a webcam. In this post, I am going to share how to code it for yourself. I'll try to share as many details as needed however, all are welcome to post comments down below.

The post will contain multiple videos which are as follows:

1) Imports Needed For FaceDetection: The video will describe the modules we need and why we need them.

2) Commands Needed For FaceDetection: I will go through the code and explain the commands used.

Below is the second video where the code is explained.

You may download the code from here