Automated Multiple Face Recognition AI Using Python

Hello, welcome to the Amazing world of Computer Vision.

Computer Vision is an AI based, that is, Artificial Intelligence based technology that allows computers to understand and label images. Its now used in Convenience stores, Driver-less Car Testing, Security Access Mechanisms, Policing and Investigations Surveillance, Daily Medical Diagnosis monitoring health of crops and live stock and so on and so forth..Even to analyze data coming from outer space stars, planets etc also we use Computer Vision.

A common example will be face detection and recognition and unlocking mechanism that you use in your mobile phone. We use that daily. That is also a big application of Computer Vision. And today, top technology companies like Amazon, Google, Microsoft, Facebook etc are investing millions and millions of Dollars into Computer Vision based research and product development.

Today, we are inundated with data of all kinds, but the plethora of photo and video data available provides the data set required to make facial recognition technology work. Facial recognition systems analyze the visual data and millions of images and videos created by high-quality Closed-Circuit Television (CCTV) cameras installed in our cities for security, smartphones, social media, and other online activity. Machine learning and artificial intelligence capabilities in the software map distinguishable facial features mathematically, look for patterns in the visual data, and compare new images and videos to other data stored in facial recognition databases to determine identity.

A Facial recognition system is a technology capable of identifying or verifying a person from a digital image. 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 Bio-metric Artificial Intelligence based application that can uniquely identify a person by analyzing patterns based on the person’s facial textures and shape.

One of the major advantages of facial recognition technology is safety and security. Law enforcement agencies use the technology to uncover criminals or to find missing children or seniors.

Airports are increasingly adding facial recognition technology to security checkpoints; the U.S. Department of Homeland Security predicts that it will be used on 97 percent of travelers by 2023. When people know they are being watched, they are less likely to commit crimes so the possibility of facial recognition technology being used could deter crime.

Facial recognition  can add conveniences. In addition to helping you tag photos in Facebook or your cloud storage via Apple and Google, you will start to be able to check-out at stores without pulling out money or credit cards—your face will be scanned. At the A.I. Bar, facial recognition technology is used to add patrons who approach the bar to a running queue to get served their drinks more efficiently.

Along with all it benefits Computer vision Industry is $20 Billion industry which will be one of the most important job markets in the years to come.

As the fastest growing language in popularity, Python is well suited to leverage the power of existing computer vision libraries to learn from all this image and video data.

So.. Learning and mastering this Face Recognition Python technology is surely up-market and it will make you proficient in competing with the swiftly changing Image Processing technology arena.

In this course we’ll teach you everything you how create a Face Recognition System which can be automated so it can add images to its data set with help of user whenever new faces are detected .

Here are the major topics that we are going to cover in this course.

Session 1: Introduction

Introduction and requirements of the course.

Session 2: Basics of Computer Vision And OpenCv

Students will have a basic understanding of computer vision and students will be able to Image Analysis and Manipulation using OpenCv.

Session 3: Introduction to Understanding Face Recognition using face_recognition library

Students will understand how face recognition works and how to implement various functions of face_recognition Library and will learn how to compare two faces using Euclidean Distance.

Session 4: Project: Automated Multiple Face Detection

Students will be able to understand and implement Automated Multiple Face detection AI

Session 5:Future Scope and Face Recognition Market

Students will understand various applications of face detection and will learn about trends in this market

At the end of the course you will be able to

• Create Automated Multiple Face Detection System

• Learn Basics of Open CV

• Use Google Collab

• Understand how face recognition works

• Understand What is computer vision and how it works

So without wasting much time, lets dive in to this magical world. See you soon in the class room.

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Frequently Asked Questions

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