A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces, typically employed to authenticate users through , works by pinpointing verification services and measuring facial features from a given image.
In this blog, we are going to see how we can perform various tasks using Facial Recognition. So, first task is to send a email to someone using facial recognition. For this we have to use SMTP protocol. The Simple Mail Transfer Protocol (SMTP) is an internet standard communication protocol for email transmission.
For doing this task using python we have to use smtplib library. This help us to transfer the email to other person without opening gmail. But to use it we have to allow access to less secure apps in google account settings.
Second task is to send Whatsapp messages to someone using facial recognition. For this we have to use a library known as pywhatkit .
This library consists of a function known as pywhatkit.sendwhatmsg() which takes arguments like phone number of the sender, message and what time you want to send the message. It will automatically open the whatsapp on the allocated time and send the message to the other person.
Third task is to launch an AWS EC2 instance using facial recognition. Before knowing how to do this , lets see what is an AWS EC2 instance. It is a commercial web service for hosting computer applications.
For launching EC2 instance using python we have to use boto3 library. Boto3 is the name of the Python SDK for AWS. It allows you to directly create, update, and delete AWS resources from your Python scripts. By using it we have to first call for EC2 resource and then create instances by providing information like image id and instance type.
Fourth task is to create 5 GB EBS volume and attach it to the instance. For this we have to use create_volume() function after creating an instance and provide details like availability zone, size, volume type and tag specification. Finally attach the EBS volume using attach_volume() function.
To see the source code of the following operations refer this github link