Face detection is a computer vision technology that helps to locate/visualize human faces in digital images. This technique is a specific use case of object detection technology that deals with detecting instances of semantic objects of a certain class (such as humans, buildings or cars) in digital images and videos. With the advent of technology, face detection has gained a lot of importance especially in fields like photography, security, and marketing.

This is the repository linked to the tutorial with the same name. The idea is to introduce people to the concept of object detection in Python using the OpenCV library and how it can be utilized to perform tasks like Facial detection.

Face Detection with Python using OpenCV

OpenCV-Python supports all the leading platforms like Mac OS, Linux, and Windows. It can be installed in either of the following ways:

1. From pre-built binaries and source :

Please refer to the detailed documentation here for Windows and here for Mac.

2. Unofficial pre-built OpenCV packages for Python.

Packages for standard desktop environments (Windows, macOS, almost any GNU/Linux distribution)

run pip install opencv-python if you need only the main modules run pip install opencv-contrib-python if you need both main and contrib modules (check extra modules listing from OpenCV documentation)

• Images as Arrays
• Images and OpenCV
• Basic Operations on Images
• Face Detection

An image is nothing but a standard Numpy array containing pixels of data points. More the number of pixels in an image, the better is its resolution. You can think of pixels to be tiny blocks of information arranged in form a 2 D grid and the depth of a pixel refers to the colour information present in it. In order to be processed by a computer, an image needs to be converted into a binary form. The colour of an image can be calculated as follows:

  Number of colours/ shades = 2^bpp where bpp represents bits per pixel.

Naturally, more the number of bits/pixels , more possible colours in the images. The following table shows the relationship more clearly.

Let us now have a look at the representation of the different kinds of images:

1. Binary Image
2. Grayscale image
3. Coloured image

In this section we will perform simple operations on images using OpenCV like opening images, drawing simple shapes on images and interacting with images through callbacks. This is necessary to create a foundation before we move towards the advanced stuff.

• Importing Images in OpenCV
• Open CV Using Jupyter notebooks
• OpenCV Using Python Scripts
• Savings images

In this section, we will learn how we can draw various shapes on an existing image to get a flavour of working with OpenCV.

• Drawing on Images
• Functions & Attributes
• Writing on Images

Face detection is a technique that identifies or locates human faces in digital images. A typical example of face detection occurs when we take photographs through our smartphones, and it instantly detects faces in the picture. Face detection is different from Face recognition. Face detection detects merely the presence of faces in an image while facial recognition involves identifying whose face it is.

Face detection is performed by using classifiers. A classifier is essentially an algorithm that decides whether a given image is positive(face) or negative(not a face). A classifier needs to be trained on thousands of images with and without faces. Fortunately, OpenCV already has two pre-trained face detection classifiers, which can readily be used in a program. The two classifiers are: Haar Classifier and Local Binary Pattern(LBP) classifier.

Haar feature-based cascade classifiers

• 1. 'Haar features' extraction
• 2. 'Integral Images' concept
• 3. 'Adaboost': to improve classifier accuracy
• 4. Using 'Cascade of Classifiers' Face Detection with OpenCV-Python