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Face Recognition Attendance System Web App Deploy in AWS

Face Recognition Attendance System Web App Deploy in AWS

Loại khoá học: Software Development Tools

Build a Comprehensive Attendance System web app using Face Recognition, Machine Learning, Redis, Python, Streamlit

image
50.000 VND
1.599.000 VND
Đầy Đủ Bài Giảng
Học Online Tiện Lợi
Kích Hoạt Nhanh 2-5 Phút
Thanh toán tự động
Được phép tải xuống

Tổng quan

Nội dung khoá học

Đánh giá

Mô tả

This course is designed to teach you how to create a Complete Attendance System using Face Recognition technology. You will learn the principles of face recognition, image processing, and machine learning algorithms that enable the creation of an accurate and reliable attendance system.


Throughout the course, you will use Python programming language and various libraries, such as OpenCV, Numpy, Pandas, Insightface, Redis to build a comprehensive attendance system. You will start by learning the basics of face detection, feature extraction, and face recognition algorithms. Then, you will integrate these algorithms with the attendance system that you will build from scratch.

By the end of the course, you will have a complete attendance system that is capable of identifying people and marking their attendance based on their facial features. This course is suitable for beginners in programming and machine learning, and no prior knowledge of face recognition is required.


Topics covered in this course include:


  • Introduction to face recognition and attendance systems

  • Basic image processing techniques

  • Feature extraction and dimensionality reduction

  • Face detection and recognition algorithms

  • Machine learning for face recognition

  • Building an attendance system with face recognition

  • Redis with Python

  • Integrate Redis and Face Recognition system.

  • Registration Form (Add new person data)

  • Streamlit for webapp

  • Real Time Prediction App

    • Registration Form

    • Report

By the end of this course, you will have a strong understanding of how to create a complete attendance system using face recognition technology. You will also have the skills to apply this knowledge to other computer vision applications.


See you inside the course.

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Yêu cầu

Nội dung khoá học

14 sections

Introduction

4 lectures
Introduction
09:25
Course Curriculum
01:57
Complete Resources
00:02
OpenCV with Python
00:52

Setting up Environment

4 lectures
[IMPORTANT] What Python version to install ?
08:52
Install appropriate Python version
05:49
Install Virtual Environment
03:12
Install Required Packages
04:03

Redis as Database Crash Course [Python]: Optional

17 lectures
Useful links
00:02
Setting up Redis cloud
08:22
Connect notebook to Redis CLI (Client) using host, port and password
03:35
Redis Data Structures
03:18
Redis: Strings commands ("set", "get")
04:52
Redis: String - SET part 2
04:54
Redis: String - Part 3
04:33
Redis: String - Part 4
03:34
Redis: String - part 5
04:37
Redis: String - part 6
04:54
Redis String: String (additional commands)
04:56
Intro to Redis with Python
10:42
Redis List
06:07
Redis List part 2
08:21
Redis List part 3
11:34
Redis List part 4
09:00
Redis List part 5
07:59

Face Recognition with InsightFace API

11 lectures
Useful Links
00:03
Automatic Fast Face Recongnition System Intro
05:04
What and Why Insightface
01:22
InsightFace Install
06:42
Import insightface & how to solve common error import error
09:10
Configure Pretrained Models of Insightface in python
10:08
Assignment Solution: Configure "bufallo_sc" model
02:19
Get Face Analysis results/report from Insightface python
04:46
Draw bounding box, Key points, Age, Gender for multiple faces part -1
07:29
Draw bounding box, Key points, Age, Gender for multiple faces part -2
11:37
Assignment Solution: bbox, keypoints, score for buffalo_sc model
09:07

Attendance System : Fast Face Recognition

28 lectures
Introduction to Attendance System and What we are building in this course
03:53
Flow Diagram of Attendance System
05:34
Get Data & Understand the folder structure of data
03:44
Fast Face Recognition: Data Preparation in Python
09:53
Fast Face Recognition (FFR): Data Preparation - Clean Text (labels)
05:38
FFR: Data Preparation - define path of all images
03:45
FFR: Data Preparation - Extract Facial Embeddings from all images
09:39
Predicting Person name part 1
09:12
Machine Learning (ML) Search Algorithm - Euclidean Distance
07:47
ML Search Algorithm - Manhattan Distance
02:32
ML Search Algorithm - Chebyshev Distance
01:49
ML Search Algorithm - Minkowski Distances
01:09
ML Search Algorithm - Cosine Similarity
06:20
Distance vs Similarity methods
04:19
ML Search Algorithm - Distance Method
06:08
ML Search Algorithm - Similarity Method
04:01
ML Search Algorithm in Python
07:05
Analyzing Euclidean , Manhattan and Cosine values for test image
10:09
Predicting Person Name with Euclidean Distance
05:10
Predicting Person Name with Manhattan Distance
02:17
Predicting Person Name with Cosine similarity
03:30
Advantages of Cosine similarity over Euclidean and Manhattan Distance.
01:55
Identify Multiple Person Name in one image part 1
04:25
Identify Multiple Person Name in one image part 2
08:15
Identify Multiple Person Name in one image part 3
11:44
Identify Multiple Person Name in one image part 4
04:27
Optimize Collected data (facial embeddings) and save
04:31
Optimize Collected data (facial embeddings) and save part 2
05:25

Attendance System : Registration Form & Integrate to Redis

6 lectures
Save Collected data into Redis Database
13:25
Save Collected data into Redis Database part 2
03:41
Idea of Registration form in Python
03:20
Registration form: Collect details of new Students and Teachers
09:50
Registration form: Collect face embedding samples for new registry
09:15
Registration form: Store information in Redis database
04:25

Attendance System : Real Time Person name detection

5 lectures
What we are developing
00:54
Preparing Python module for Real time prediction
14:44
Retrieve data from database
06:42
Real Time Person Name prediction
05:03
Real Time Person Name Prediction part 2
02:03

WEB APP Installations

2 lectures
Install Visual Studio Code
03:34
Install required libraries
06:33

Attendance Web App

17 lectures
Streamlit App Intro
01:32
Create Home and connect all Pages from Home page
06:28
Import face_rec into app and retrive data from Redis
09:16
Apply Spinner to face_rec and reduce the time to start the app
05:37
Real Time Person name detection using streamlit webrtc
08:42
Find time at which person name is detected
06:59
Save Logs (person name and time) in Redis database
03:04
Save Logs (person name and time) in Redis database part 2
19:40
Show Logs in Streamlit Report
05:44
Show Logs: Add refresh button
01:13
Show Logs: Create tabs for Registered users and Logs
03:21
Testing logs
02:26
Registration Form part 1
08:26
Registration Form Part 2
17:28
Registration Form part 3
05:09
Registration Form part 4
17:35
Testing Registration form
06:00

Additional Lectures

1 lectures
How to delete User (Students or Teachers) Records in "academy:register"
06:41

Deploy Streamlit App in AWS EC2 instance

12 lectures
Install GIT
01:17
Create account in AWS
07:59
STEP by STEP process to Deploy our streamlit in AWS EC2 Instance
00:05
Step -1: Setup the code for deployment.
16:43
Step - 2: Step up Streamlit to sever as HTTPS and STUN server for webrtc
10:17
Step -3: Push code to Github Repository
05:46
Step -4: Create EC2 Instance
08:19
Step -4 cont..: Run Streamlit App in EC2 Instance
16:08
Step -5: Configure HTTPS with Apache2 Server
08:27
Step -5 cont: Configure HTTPS with Apache2 Server
12:17
Step -6: Production with PM2
06:39
Step -7: Add UDP Inbound rules for STUN server in EC2
04:12

Mark "In Time", "Out Time", "Present / Absent" from logs

5 lectures
Pre-process Logs data
12:25
In-Time and Out-time
09:40
Mark Present or Absent
16:00
Simulated Logs
00:21
Update
00:03

Additional topics

1 lectures
Additional Code with Authenticator (Login)
00:57

BONUS

1 lectures
Bonus Lecture
00:13

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