Published 3/2023MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 4.40 GB | Duration: 8h 43m
Build a Comprehensive Attendance System using Face Recognition and Machine Learning What you'll learn Real 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 Live Attendance System App-2: Registration Form for new teachers and students App-3: Reporting Requirements At least bner to Python Atleast beer 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 bners 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 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 Person name detection Lecture 62 What we are developing Lecture 63 Preparing Python module for Real prediction Lecture 64 Retrieve data from database Lecture 65 Real Person Name prediction Lecture 66 Real 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 to start the app Lecture 73 Real Person name detection using streamlit webrtc Lecture 74 Find at which person name is detected Lecture 75 Save Logs (person name and ) in Redis database Lecture 76 Save Logs (person name and ) 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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