Face Recognition System - OpenCV - Python

A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source. A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source. There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from given image with faces within a database. It is also described as a Biometric Artificial Intelligence based application that can uniquely identify a person by analysing patterns based on the person's facial textures and shape.

Working Video Link : https://www.linkedin.com/feed/update/urn:li:activity:6416197338695495680

The 3 Phases

To create a complete project on Face Recognition, we must work on 3 very distinct phases:

1. Face Detection and Data Gathering
2. Train the Recognizer
3. Face Recognition

BACKGROUND ON FACE RECOGNITION

When you look at an apple, your mind immediately tells you: that is an apple. This process is recognition in the simplest of terms. So, what’s facial recognition? The same, but for faces, obviously.

But, the real question is:

How can a computer recognize a face?

Take a real life example:

When you meet someone for the first time, you don’t know who that person is at once, right? While he's talking to you or shaking your hand, you’re looking at his face: eyes, nose, mouth, skin tone… This process is your mind gathering data and training for face recognition.

Next, that person tells you that his name is RISHABH. So, your brain has already gotten the face data, and now it has learned that this data belongs to RISHABH.

The next time you see RISHABH or see a picture of his face, your mind will follow this exact process:

1. Face Detection: Look at the picture and find a face in it.

2. Data Gathering: Extract unique characteristics of RISHABH’s face that it can use to differentiate him from another person, like eyes, mouth, nose, etc.

3. Data Comparison: Despite variations in light or expression, it will compare those unique features to all the features of all the people you know.

4. Face Recognition: It will determine “Hey, that’s my boy RISHABH!”