Skip to content

AdarshaHalder/face-recognition

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

WhoIsComingTo.Party

Featured on Hacker News

Demo

https://whoiscomingto.party

Installation

cd functions && npm i && npm run setup

firebase deploy

About

With all the craze about Machine Learning nowadays, I wish to try my hands on this technology especially Face Recognition. But unfortunately if you are just a humble software developer it will be a long journey. But luckily when I'm scrolling around in Github, I found this awesome library which is called face-api.js.

FAQ

  1. What are the necessary files needed to use the library?
  • You need the Tensorflow saved models. Luckily for you these are already saved in the library repo in weights folder so you can just copy from it.
  1. What kind of data that I will store in database?
  • It is called descriptor. The data is in array of 128 float numbers e.g [-0.029340924695134163, -0.13368940353393555, 0.1174287348985672, ... (total 128) ]. One people can have many descriptor to make the recognition more accurate. So when you are retrieving the data from your database, you might want to output into following JSON format;
	[
		{
			label: 'person 1',
			descriptors: [descriptor1array]
		},
		{
			label: 'another person',
			descriptors: [descriptor2array, descriptor3array]
		}
	]
  1. Do I really need to make some face expressions in the data training to make the recognition more accurate?
  • No, I just having fun since the face expression recognition function is available. Plus it also work like some kind of authorisation since anyone can take your picture, but no one can make you making those face expressions. Unless you are a famous person and your picture in different expression is everywhere online.

License

Licensed under the MIT license

About

Simple Face Recognition Attendance System

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • JavaScript 58.6%
  • HTML 28.9%
  • CSS 10.0%
  • Shell 2.5%