Complete Attendance System With Face Recognition

Free Download 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 3MPORTANT 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.


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