Complete Attendance System With Face Recognition

Build a Comprehensive Attendance System using Face Recognition and Machine Learning

What you'll learn

Real Time Live Attendance System

Detect and Idenify person name and person role with Face Recognition

Develop 3 Streamlit Web App

Integrate Face Recognition Model with Redis Database

Learn about Redis with Python

App-1: Real Time Live Attendance System

App-2: Registration Form for new teachers and students

App-3: Reporting

Requirements

At least beginner to Python

Atleast begineer on Pandas, Numpy and OpenCV libraries

Description

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 systemsBasic image processing techniquesFeature extraction and dimensionality reductionFace detection and recognition algorithmsMachine learning for face recognitionBuilding an attendance system with face recognitionRedis with PythonIntegrate Redis and Face Recognition system.Registration Form (Add new person data)Streamlit for webappReal Time Prediction AppRegistration FormReportBy 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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Complete Resources

Section 2: Setting up Environment

Lecture 3 [IMPORTANT] What Python version to install ?

Lecture 4 Install appropriate Python version

Lecture 5 Install Virtual Environment

Lecture 6 Install Required Packages

Section 3: Fundamentals of Redis

Lecture 7 Setting up Redis cloud

Lecture 8 Connect notebook to Redis CLI (Client) using host, port and password

Lecture 9 Redis Data Structures

Lecture 10 Redis: Strings commands ("set", "get")

Lecture 11 Redis: String - SET part 2

Lecture 12 Redis: String - Part 3

Lecture 13 Redis: String - Part 4

Lecture 14 Redis: String - part 5

Lecture 15 Redis: String - part 6

Lecture 16 Redis String: String (additional commands)

Section 4: Redis with Python

Lecture 17 Intro to Redis with Python

Section 5: InsightFace API

Lecture 18 Automatic Fast Face Recongnition System Intro

Lecture 19 What and Why Insightface

Lecture 20 InsightFace Install

Lecture 21 Import insightface & how to solve common error import error

Lecture 22 Configure Pretrained Models of Insightface in python

Lecture 23 Assignment Solution: Configure "bufallo_sc" model

Lecture 24 Get Face Analysis results/report from Insightface python

Lecture 25 Draw bounding box, Key points, Age, Gender for multiple faces part -1

Lecture 26 Draw bounding box, Key points, Age, Gender for multiple faces part -2

Lecture 27 Assignment Solution: bbox, keypoints, score for buffalo_sc model

Section 6: Attendance System : Fast Face Recognition

Lecture 28 Introduction to Attendance System and What we are building in this course

Lecture 29 Flow Diagram of Attendance System

Lecture 30 Get Data & Understand the folder structure of data

Lecture 31 Fast Face Recognition: Data Preparation in Python

Lecture 32 Fast Face Recognition (FFR): Data Preparation - Clean Text (labels)

Lecture 33 FFR: Data Preparation - define path of all images

Lecture 34 FFR: Data Preparation - Extract Facial Embeddings from all images

Lecture 35 Predicting Person name part 1

Lecture 36 Machine Learning (ML) Search Algorithm - Euclidean Distance

Lecture 37 ML Search Algorithm - Manhattan Distance

Lecture 38 ML Search Algorithm - Chebyshev Distance

Lecture 39 ML Search Algorithm - Minkowski Distances

Lecture 40 ML Search Algorithm - Cosine Similarity

Lecture 41 Distance vs Similarity methods

Lecture 42 ML Search Algorithm - Distance Method

Lecture 43 ML Search Algorithm - Similarity Method

Lecture 44 ML Search Algorithm in Python

Lecture 45 Analyzing Euclidean , Manhattan and Cosine values for test image

Lecture 46 Predicting Person Name with Euclidean Distance

Lecture 47 Predicting Person Name with Manhattan Distance

Lecture 48 Predicting Person Name with Cosine similarity

Lecture 49 Advantages of Cosine similarity over Euclidean and Manhattan Distance.

Lecture 50 Identify Multiple Person Name in one image part 1

Lecture 51 Identify Multiple Person Name in one image part 2

Lecture 52 Identify Multiple Person Name in one image part 3

Lecture 53 Identify Multiple Person Name in one image part 4

Lecture 54 Optimize Collected data (facial embeddings) and save

Lecture 55 Optimize Collected data (facial embeddings) and save part 2

Section 7: Attendance System : Registration Form & Integrate to Redis

Lecture 56 Save Collected data into Redis Database

Lecture 57 Save Collected data into Redis Database part 2

Lecture 58 Idea of Registration form in Python

Lecture 59 Registration form: Collect details of new Students and Teachers

Lecture 60 Registration form: Collect face embedding samples for new registry

Lecture 61 Registration form: Store information in Redis database

Section 8: Attendance System : Real Time Person name detection

Lecture 62 What we are developing

Lecture 63 Preparing Python module for Real time prediction

Lecture 64 Retrieve data from database

Lecture 65 Real Time Person Name prediction

Lecture 66 Real Time Person Name Prediction part 2

Section 9: WEB APP Installations

Lecture 67 Install Visual Studio Code

Lecture 68 Install required libraries

Section 10: Attendance Web App

Lecture 69 Streamlit App Intro

Lecture 70 Create Home and connect all Pages from Home page

Lecture 71 Import face_rec into app and retrive data from Redis

Lecture 72 Apply Spinner to face_rec and reduce the time to start the app

Lecture 73 Real Time Person name detection using streamlit webrtc

Lecture 74 Find time at which person name is detected

Lecture 75 Save Logs (person name and time) in Redis database

Lecture 76 Save Logs (person name and time) in Redis database part 2

Lecture 77 Show Logs in Streamlit Report

Lecture 78 Show Logs: Add refresh button

Lecture 79 Show Logs: Create tabs for Registered users and Logs

Lecture 80 Testing logs

Lecture 81 Registration Form part 1

Lecture 82 Registration Form Part 2

Lecture 83 Registration Form part 3

Lecture 84 Registration Form part 4

Lecture 85 Testing Registration form

Section 11: BONUS

Lecture 86 Bonus Lecture

Anyone who like to develop End to End Face Recognition based Attendance System.