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Remote Employee Login with Facial Recognition -Next ERP

In the era of remote work, secure and efficient authentication mechanisms are critical. This project focuses on integrating facial recognition and liveness detection into Next ERP to enable secure, contactless login for remote employees. By leveraging advanced AI techniques and optimizing processing times, the solution ensures user convenience without compromising security.

Challenges

  • Manual Authentication Issues:

    Manual authentication methods are prone to human errors, delays, and potential security breaches.
    Inconsistent processes for remote employee identity verification.

  • Performance Optimization:

    Initial facial recognition and liveness detection workflows required up to 16 seconds, leading to usability concerns.

  • Scalability for Enterprise Use:

    Need for a solution capable of handling a high number of concurrent users without affecting performance.

  • Integration with ERP Systems:

    Ensuring seamless integration with Next ERP for user management, enrollment, and access control.

Our Solutions

  • Facial Recognition and Liveness Detection:

    Built using Python DLIB DNN model, leveraging deep learning for high-accuracy face detection and recognition.
    Liveness detection ensures that login attempts are made by real users and not spoofed through static images or videos.

  • Time Optimization:

    Optimized the face recognition and liveness detection process to complete within 2.5 seconds, a significant improvement from the initial 16 seconds.
    Implemented model pruning and efficient inference pipelines to reduce latency.

  • Seamless ERP Integration:

    Integrated the solution into Next ERP to enable user enrollment, authentication, and access control directly from the platform.
    Ensured the facial recognition module aligns with existing user data workflows.

  • Scalable Cloud-Based Deployment:

    Deployed on AWS Server to handle high concurrency and ensure scalability for large enterprises.

AWS Server

Frappe

Python

Impacts

  • User Enrollment:

    Employees upload their photos for initial enrollment via the ERP interface.
    The system verifies and stores facial data securely, linking it to the employee’s profile.

  • Login Process:

    Employees log in by showing their faces to the webcam.
    The system performs:
    Face detection and recognition using DLIB DNN.
    Liveness detection to validate authenticity.
    Authentication completes within 2.5 seconds.

  • Monitoring and Management:

    Admins use the Next ERP dashboard to manage users, view login logs, and monitor system performance.

Benefits

  • Enhanced Security:

    Eliminates risks associated with password-based authentication.
    Liveness detection prevents spoofing attempts.

  • Improved Performance:

    Optimized workflows reduce login time from 16 seconds to 2.5 seconds, improving user experience.

  • Scalable and Reliable:

    Cloud-based deployment on AWS ensures scalability for enterprises of all sizes.

  • Seamless Integration:

    Built into Next ERP, allowing centralized management of authentication workflows.

Future Scope

  • Advanced AI Models:

    Incorporate transformer-based models for even faster and more accurate recognition.

  • Multi-Factor Authentication (MFA):

    Combine facial recognition with other authentication factors (e.g., OTP) for enhanced security.

  • Global Deployment:

    Extend support for multi-region deployments with localization for different languages and compliance standards.

Conclusion

The Remote Employee Login using Facial Recognition system is a game-changer for enterprise-level authentication. By combining facial recognition with liveness detection and optimizing the process for speed, it ensures secure, fast, and reliable login experiences for remote employees. The seamless integration with Next ERP and scalable deployment on AWS makes it a robust and future-ready solution.