Face Attendance Recognition System

Overview

The Face Attendance Recognition System is an end-to-end solution that automates attendance logging using facial recognition technology. This project includes a fully integrated user interface, data storage, and cloud-based machine learning for facial recognition. It is designed to streamline attendance tracking for educational institutions or organizations.

Features

• User Authentication: Secure login system using hashed passwords.

• Face Recognition: Captures and identifies faces using AWS Rekognition.

• Attendance Logging: Records attendance in AWS DynamoDB with unique composite keys.

• Real-Time Dashboard: Built with Streamlit for easy access and management of attendance data.

• Attendance Reports: Generate and download detailed reports of attendance records.

• Data Management: Functions to insert, retrieve, and update student, teacher, and course data in DynamoDB.

Click for access: https://priscilaprogramer07-facial-recognition-a-dashboardinicio-agrhui.streamlit.app/

Technologies Used

• Streamlit: For creating an interactive web-based dashboard.

• Pandas: For handling and processing data from CSV files.

• Boto3: To interact with AWS services like Rekognition and DynamoDB.

• AWS Rekognition: Facial recognition service for identifying students.

• AWS DynamoDB: NoSQL database for storing data related to students, teachers, courses, and attendance records.

• AWS S3: Optional, for storing images.

• bcrypt: For hashing passwords to secure user credentials.

• Python Standard Libraries:

  • os: For handling environment variables and file paths.

  • io: For managing image data.

  • datetime: For date and time handling.

Project Structure

Facial_Recognition_Attendance_System/

│

├── Dashboard/

│ ├── dashboard.py # Main Streamlit app

│ ├── login.py # Authentication and session management

│ ├── usuarios.csv # User data CSV (now pulled from GitHub)

│ └── utils.py # Utility functions for handling DynamoDB operations

│

├── Database/

│ ├── jsons/

│ │ ├── courses.json # Sample data for courses

│ │ └── students.json # Sample data for students

│ ├── populate_data.py # Script for populating DynamoDB tables

│

├── Pages/

│ ├── inicio.py # Page for viewing the student list

│ ├── pagina1.py # Attendance page

│ ├── pagina2.py # New student registration page

│ ├── pagina3.py # Attendance report page

│ └── pagina4.py # About page

│

└── requirements.txt # Python package dependencies

Setup Instructions

Prerequisites

• Python >= 3.8+

• AWS Account with IAM roles configured for Rekognition and DynamoDB access.

• Git to clone the repository.

Step-by-Step Guide

1. Clone the repository:

git clone https://github.com/your-repo/Facial_Recognition_Attendance_System.git

cd Facial_Recognition_Attendance_System

2. Set up a virtual environment (recommended):

   
   python -m venv .venv

source .venv/bin/activate # On Windows use .venv\Scripts\activate

3.  **Install dependencies**
```bash
pip install -r requirements.txt

4. Set up AWS credentials:

Ensure your AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_DEFAULT_REGION are set as environment variables. You can add them to your ~/.bashrc, ~/.zshrc, or use a .env file.

5. Run the Streamlit app:

   streamlit run Dashboard/dashboard.py

Usage

1. Login: Enter your credentials to access the dashboard.

2. Attendance Marking: Use the camera input to capture an image and mark attendance.

3. Attendance Reports: View and download attendance summaries for specific date ranges.

4. New Student Registration: Add new student details to the system.

5. Logout: Use the logout button to end your session.

Sample Data

Use the provided JSON files in Database/jsons/ to populate your database for testing:

• courses.json: Contains sample course data.

• students.json: Contains sample student data.

Security Considerations

• Ensure that sensitive data, such as AWS credentials, are stored securely and not exposed in your code.

• Use bcrypt for password hashing to enhance the security of user authentication.

Future Improvements

• Implement real-time facial recognition for continuous attendance tracking.

• Add more detailed analytics for attendance trends.

• Integrate additional user roles and permissions.

Author

Developed by

• Data Engineers: Priscila Tzuc, Julio Dzul

• Data Scientists: Antonio Ruiz, Luis Martinez

• Data Analysts: Angel Campos, Angel Sansores

AWS Commands

Install aws-shell

pip  install  aws-shell

Configure AWS CLI

aws  configure

Create a Collection on AWS Rekognition

aws  rekognition  create-collection  --collection-id  facerecognition_collection  --region  us-east-1

Create a Table on DynamoDB

aws  dynamodb  create-table  --table-name  facerecognition  \

--attribute-definitions  AttributeName=RekognitionId,AttributeType=S  \

--key-schema  AttributeName=RekognitionId,KeyType=HASH  \

--provisioned-throughput  ReadCapacityUnits=1,WriteCapacityUnits=1  \

--region  us-east-1

Create an S3 Bucket

aws  s3  mb  s3://bucket-name  --region  us-east-1