Free Download Complete Face Recognition Attendance System Using Knn
Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 462.21 MB | Duration: 1h 2m
Complete Face Recognition Attendance System Using KNN & OPENCV
What you'll learn
Fundamentals of face recognition technology and its practical applications.
Implementation of the K-Nearest Neighbors (KNN) algorithm for face recognition.
Data collection, preprocessing, and feature extraction techniques for facial recognition.
Integration of face recognition technology into attendance systems for automated attendance recording.
Requirements
BASIC PYTHON AND OPENCV IS REQUIRED
Description
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) algorithm. Face recognition technology has gained significant traction in various industries, including education, security, and workforce management. By the end of this course, you will have the skills and knowledge to develop a fully functional attendance system that can accurately identify and record individuals' attendance using facial recognition technology.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.Preprocess the face images by resizing, cropping, and normalizing them to ensure consistency and accuracy in recognition.Feature Extraction and Representation:Extract facial features from the preprocessed images using techniques like Principal Component Analysis (PCA) or Local Binary Patterns (LBP).Represent the facial features as feature vectors suitable for input to the KNN algorithm.Implementing the KNN Algorithm:Understand the principles of the K-Nearest Neighbors (KNN) algorithm for classification.Implement the KNN algorithm using Python and scikit-learn library for face recognition.Training and Evaluation:Split the dataset into training and testing sets and train the KNN classifier on the training data.Evaluate the performance of the face recognition system using metrics such as accuracy, precision, and recall.Integration with Attendance System
evelop a user-friendly interface for the attendance system using graphical user interface (GUI) tools like Tkinter or PyQt.Integrate the trained KNN classifier into the attendance system to recognize faces and record attendance.Testing and Deployment:Test the face recognition attendance system with real-world data and scenarios to ensure functionality and accuracy.Deploy the attendance system for practical use in educational institutions, businesses, or other organizations.Enroll now and unlock the potential of face recognition technology for attendance management with the Complete Face Recognition Attendance System Using KNN course!Overview
Section 1: Introduction To Complete Face Recognition Attendance System Using KNN
Lecture 1 Introduction To Course
Lecture 2 Introduction To Machine Learning
Section 2: DATASET MODULE - Complete Face Recognition Attendance System Using KNN
Lecture 3 DATASET MODULE CLASS 1 : IMPORT PACKAGES
Lecture 4 DATASET MODULE CLASS 2 : IMPORT DATASET & OPENCV
Lecture 5 DATASET MODULE CLASS 3 : OUTPUT & EXPLANATION
Section 3: ATTENDANCE MODULE - Complete Face Recognition Attendance System Using KNN
Lecture 6 ATTENDANCE MODULE CLASS 1 : IMPORT PACKAGES
Lecture 7 ATTENDANCE MODULE CLASS 2 : IMPORT DATASET & OPENCV
Lecture 8 ATTENDANCE MODULE CLASS 3 : TRAIN DATASET USING KNN
Lecture 9 ATTENDANCE MODULE CLASS 4 : OUTPUT & CONCLUSION
Students and professionals interested in machine learning, computer vision, and biometric technologies.,Educators, administrators, and HR professionals seeking to implement automated attendance systems in their organizations.
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