Face Recognition Attendance System Web App Deploy in AWS

Face Recognition Attendance System Web App Deploy in AWS, available at $84.99, has an average rating of 4.55, with 116 lectures, based on 173 reviews, and has 874 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who like to develop End to End Face Recognition based Attendance System. It is particularly useful for Anyone who like to develop End to End Face Recognition based Attendance System.

Enroll now: Face Recognition Attendance System Web App Deploy in AWS

Summary

Title: Face Recognition Attendance System Web App Deploy in AWS

Price: $84.99

Average Rating: 4.55

Number of Lectures: 116

Number of Published Lectures: 116

Number of Curriculum Items: 116

Number of Published Curriculum Objects: 116

Original Price: $174.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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 systems

Basic image processing techniques

Feature extraction and dimensionality reduction

Face detection and recognition algorithms

Machine learning for face recognition

Building an attendance system with face recognition

Redis with Python

Integrate Redis and Face Recognition system.

Registration Form (Add new person data)

Streamlit for webapp

Real Time Prediction App

Registration Form

Report

By 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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course Curriculum

Lecture 3: Complete Resources

Lecture 4: OpenCV with Python

Chapter 2: Setting up Environment

Lecture 1: [IMPORTANT] What Python version to install ?

Lecture 2: Install appropriate Python version

Lecture 3: Install Virtual Environment

Lecture 4: Install Required Packages

Chapter 3: Redis as Database Crash Course [Python]: Optional

Lecture 1: Useful links

Lecture 2: Setting up Redis cloud

Lecture 3: Connect notebook to Redis CLI (Client) using host, port and password

Lecture 4: Redis Data Structures

Lecture 5: Redis: Strings commands (set, get)

Lecture 6: Redis: String – SET part 2

Lecture 7: Redis: String – Part 3

Lecture 8: Redis: String – Part 4

Lecture 9: Redis: String – part 5

Lecture 10: Redis: String – part 6

Lecture 11: Redis String: String (additional commands)

Lecture 12: Intro to Redis with Python

Lecture 13: Redis List

Lecture 14: Redis List part 2

Lecture 15: Redis List part 3

Lecture 16: Redis List part 4

Lecture 17: Redis List part 5

Chapter 4: Face Recognition with InsightFace API

Lecture 1: Useful Links

Lecture 2: Automatic Fast Face Recongnition System Intro

Lecture 3: What and Why Insightface

Lecture 4: InsightFace Install

Lecture 5: Import insightface & how to solve common error import error

Lecture 6: Configure Pretrained Models of Insightface in python

Lecture 7: Assignment Solution: Configure bufallo_sc model

Lecture 8: Get Face Analysis results/report from Insightface python

Lecture 9: Draw bounding box, Key points, Age, Gender for multiple faces part -1

Lecture 10: Draw bounding box, Key points, Age, Gender for multiple faces part -2

Lecture 11: Assignment Solution: bbox, keypoints, score for buffalo_sc model

Chapter 5: Attendance System : Fast Face Recognition

Lecture 1: Introduction to Attendance System and What we are building in this course

Lecture 2: Flow Diagram of Attendance System

Lecture 3: Get Data & Understand the folder structure of data

Lecture 4: Fast Face Recognition: Data Preparation in Python

Lecture 5: Fast Face Recognition (FFR): Data Preparation – Clean Text (labels)

Lecture 6: FFR: Data Preparation – define path of all images

Lecture 7: FFR: Data Preparation – Extract Facial Embeddings from all images

Lecture 8: Predicting Person name part 1

Lecture 9: Machine Learning (ML) Search Algorithm – Euclidean Distance

Lecture 10: ML Search Algorithm – Manhattan Distance

Lecture 11: ML Search Algorithm – Chebyshev Distance

Lecture 12: ML Search Algorithm – Minkowski Distances

Lecture 13: ML Search Algorithm – Cosine Similarity

Lecture 14: Distance vs Similarity methods

Lecture 15: ML Search Algorithm – Distance Method

Lecture 16: ML Search Algorithm – Similarity Method

Lecture 17: ML Search Algorithm in Python

Lecture 18: Analyzing Euclidean , Manhattan and Cosine values for test image

Lecture 19: Predicting Person Name with Euclidean Distance

Lecture 20: Predicting Person Name with Manhattan Distance

Lecture 21: Predicting Person Name with Cosine similarity

Lecture 22: Advantages of Cosine similarity over Euclidean and Manhattan Distance.

Lecture 23: Identify Multiple Person Name in one image part 1

Lecture 24: Identify Multiple Person Name in one image part 2

Lecture 25: Identify Multiple Person Name in one image part 3

Lecture 26: Identify Multiple Person Name in one image part 4

Lecture 27: Optimize Collected data (facial embeddings) and save

Lecture 28: Optimize Collected data (facial embeddings) and save part 2

Chapter 6: Attendance System : Registration Form & Integrate to Redis

Lecture 1: Save Collected data into Redis Database

Lecture 2: Save Collected data into Redis Database part 2

Lecture 3: Idea of Registration form in Python

Lecture 4: Registration form: Collect details of new Students and Teachers

Lecture 5: Registration form: Collect face embedding samples for new registry

Lecture 6: Registration form: Store information in Redis database

Chapter 7: Attendance System : Real Time Person name detection

Lecture 1: What we are developing

Lecture 2: Preparing Python module for Real time prediction

Lecture 3: Retrieve data from database

Lecture 4: Real Time Person Name prediction

Lecture 5: Real Time Person Name Prediction part 2

Chapter 8: WEB APP Installations

Lecture 1: Install Visual Studio Code

Lecture 2: Install required libraries

Chapter 9: Attendance Web App

Lecture 1: Streamlit App Intro

Lecture 2: Create Home and connect all Pages from Home page

Lecture 3: Import face_rec into app and retrive data from Redis

Lecture 4: Apply Spinner to face_rec and reduce the time to start the app

Lecture 5: Real Time Person name detection using streamlit webrtc

Lecture 6: Find time at which person name is detected

Lecture 7: Save Logs (person name and time) in Redis database

Lecture 8: Save Logs (person name and time) in Redis database part 2

Lecture 9: Show Logs in Streamlit Report

Lecture 10: Show Logs: Add refresh button

Lecture 11: Show Logs: Create tabs for Registered users and Logs

Lecture 12: Testing logs

Lecture 13: Registration Form part 1

Lecture 14: Registration Form Part 2

Instructors

G Sudheer
Instructor

datascience Anywhere
Team of Engineers

Brightshine Learn
Instructor Team

Rating Distribution

1 stars: 2 votes

2 stars: 0 votes

3 stars: 15 votes

4 stars: 46 votes

5 stars: 110 votes

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