Ilios Digital

Greater Chennai Corporation (GCC)

Project Title:  Face Recognition Attendance System

Description

The Greater Chennai Corporation (GCC) needs a mobile app for 20,000 municipal staff with facial recognition and geotagging for attendance tracking. Ilios digital designed a scalable, high-availability solution with zero downtime, handling up to 30,000 users with <20s latency based on AWS.

Attendance Management

Successfully implemented a mobile app for tracking attendance via facial recognition and geotagging. Achieved 99% accuracy in attendance recording.


Scalability & Performance

The system supported 30,000 concurrent users with <20s latency during peak periods.

High Availability & Reliability

Maintained 99.999% service availability with zero downtime.


Monitoring & User Satisfaction

Real-time monitoring with Amazon CloudWatch ensured optimal performance. Positive feedback and high engagement rates from users

Project Outcomes

Problem Statement

The key challenges GCC faced -
  • Efficiently managing attendance across 20,000 staff members spread over a 425 sq km area.
  • Implementing a scalable system with low latency (<20s), capable of handling 30,000 users.
  • Ensuring robust monitoring, fault tolerance, and high availability
  • Guaranteeing zero downtime in a mission-critical solution.

    • Use Case & Architecture

    The proposed solution for the Greater Chennai Corporation's attendance system involves a Mobile Application for NULM staff, utilizing Amazon Recognition for facial recognition and Amazon RDS for storing user profiles, attendance, and geotagging data. An API Gateway serves as the entry point for mobile app requests, while AWS Lambda manages backend processing. Elastic Load Balancer (ELB) ensures traffic distribution, and Amazon CloudFront delivers static content with low latency. Amazon CloudWatch provides real-time monitoring and performance alerts to maintain system reliability and availability. Elastic Load Balancer (ELB): Distributes traffic for high concurrency.

    • Mobile Application (Android/iOS):
      • Captures facial images and geolocation.
      • Secures API traffic using RESTful services.
      • Handles authentication and authorization.
    • Amazon Rekognition:
      • Performs facial recognition for attendance verification.
    • Amazon RDS (MySQL/PostgreSQL):
      • Stores attendance records, user profiles, and geotagging data.
      • Utilizes read replicas for high-read loads and automated backups.
    • Amazon S3:
      • Stores facial images for processing.
      • Logs for auditing and analysis.
    • Elastic Load Balancer (ELB):
      • Distributes incoming traffic to ensure high availability and fault tolerance.
      • Supports high concurrency.
    • Amazon CloudFront:
      • Delivers static content with low latency across distributed locations.
    • Amazon CloudWatch:
      • Monitors API Gateway, AWS Lambda functions, and RDS performance.
      • Provides real-time monitoring and performance alerts.
    • AWS Lambda:
      • Manages backend processing for mobile app interactions.
    • Amazon API Gateway:
      • Serves as the entry point for mobile application requests.

    AWS Services for the Solution

    • Compute:
      • AWS Lambda and EC2 Auto Scaling to handle dynamic traffic loads efficiently.
    • Storage:
      • Amazon S3 for storing facial images and logs.
      • Amazon RDS for managing transactional attendance and user profile data.
    • Content Delivery:
      • Amazon CloudFront for caching and delivering static assets with low latency.
    • Security:
      • IAM roles to manage access with least privilege.
      • Data encryption both at rest and in transit.
      • AWS Secrets Manager to securely manage and retrieve sensitive credentials.
    • Monitoring:
      • Amazon CloudWatch for real-time performance monitoring and alerts.

    Conclusion

    The implementation of the Face Recognition Attendance System for the Greater Chennai Corporation stands as a robust example of how AWS-native solutions can deliver scalable, secure, and high-availability applications for mission-critical public sector needs. Ilios Digital successfully addressed the core challenges of managing attendance for 20,000+ geographically distributed municipal staff by designing a cloud-native system that ensures real-time performance, high fault tolerance, and operational reliability.

    Leveraging services such as Amazon Rekognition, RDS, Lambda, API Gateway, CloudFront, and CloudWatch, the solution achieved 99% attendance accuracy, sub-20 second latency even during peak usage, and 99.999% service uptime with zero downtime. This project not only enhanced operational efficiency for GCC but also demonstrated the value of cloud architecture best practices including auto-scaling, multi-AZ redundancy, secure data handling, and blue/green deployments.

    The successful execution of this project reinforces the strategic advantage of adopting AWS-native technologies to drive digital transformation in governance, ensuring transparency, efficiency, and user satisfaction.