Face Recognition Attendance Project Using Machine Learning

About this course

Course Description:Welcome to the "Complete Face Recognition Attendance System Using KNN" course! In this hands-on project-based course, you will learn how to build a comprehensive face recognition attendance system using the K-Nearest Neighbors (KNN) In this hands-on course, you’ll learn how to create a powerful Face Recognition Attendance system that detects and marks attendance automatically using live webcam input. Whether you're a beginner or an enthusiast in computer vision, this course will help you master every step of the Face Recognition Attendance workflow.We’ll start with face detection, proceed to face encoding and recognition, and then build the logic to automate Face Recognition Attendance using Python and OpenCV. You’ll also learn how to store attendance records securely in CSV or database files as part of your Face Recognition Attendance project.By the end of the course, you’ll have built a complete Face Recognition Attendance system, ideal for classrooms, offices, or security use cases. This practical project will be a great addition to your portfolio and skill set.Class Overview:Introduction to Face Recognition Technology:Understand the basics of face recognition technology and its applications.Explore different face recognition algorithms and their strengths and weaknesses.Setting Up the Development Environment:Install necessary libraries and dependencies, including OpenCV and scikit-learn, for face recognition and KNN algorithm implementation.Set up the development environment and create a new project directory.Data Collection and Preprocessing:Collect face images from various sources and individuals to create a dataset for training.<

Course objectives

• build a complete face recognition attendance system
• understand different face recognition algorithms
• set up a development environment with necessary libraries
• collect and preprocess data for machine learning