Face Recognition Attendance System Web App Deploy in AWS

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