/authloop

Authloop

OpenSource Biometric SDK for securing fintech applications.

Tech Stack

  • Python: Serves as the primary programming language due to its extensive support for data processing, machine learning, and integration capabilities.
  • OpenCV: Utilized for facial recognition features, providing a powerful suite of tools for image processing and computer vision.
  • SciPy and NumPy: For advanced mathematical computations, signal processing, and linear algebra operations which are essential in processing biometric data.

Machine Learning and AI

  • TensorFlow/Keras: For developing and training machine learning models for both face and voice recognition. TensorFlow offers a flexible platform for deep learning, while Keras provides a high-level API to speed up development.
  • Librosa: A Python library for audio analysis, perfect for extracting features from voice data, which is crucial for audio biometric verification.

Database and Storage

  • PostgreSQL: A robust and secure relational database to store user data, biometric templates, and authentication logs.
  • Amazon S3: For secure and scalable cloud storage of biometric data, ensuring data integrity and accessibility.

Backend and API Development

  • Flask/Django: Flask for creating lightweight microservices for different biometric processes, or Django for a more structured approach with its built-in features for rapid development and deployment.
  • RESTful API: Designing APIs for client-server communication, enabling seamless integration with mobile and web applications.

Security and Compliance

  • JWT (JSON Web Tokens): For secure transmission of information between parties as a JSON object, crucial for authentication and information exchange.
  • OAuth 2.0: For secure authorization workflows, especially when integrating third-party services.
  • SSL/TLS: To encrypt data in transit, protecting sensitive biometric data and user information.

Deployment and Operations

  • Docker: For containerizing the application, ensuring consistency across different development and production environments.
  • Kubernetes: For orchestrating container deployment, scaling, and management, particularly useful for handling high-traffic applications.
  • AWS/GCP: As cloud service providers for hosting, computing, and database services, offering scalability, reliability, and global reach.

Development and Monitoring Tools

  • Git: For version control, facilitating collaborative development and code management.
  • Sentry/New Relic: For real-time error tracking and application performance monitoring, ensuring high availability and reliability.

Authloop Architecture Design

1. User Interface (UI) Layer

  • Mobile and Web Applications: Users interact with Authloop through mobile apps or web applications. This layer is responsible for capturing biometric data (face images and voice samples) and displaying authentication results.

2. Application Programming Interface (API) Layer

  • Authentication API: Serves as the intermediary for processing authentication requests, receiving biometric data from the UI, and sending commands to the backend services.
  • Data Management API: Handles user data, including registration details and biometric templates, ensuring secure storage and retrieval.

3. Application Logic Layer

  • Biometric Processing Service:
    • Face Recognition Service: Utilizes OpenCV and TensorFlow for detecting, analyzing, and comparing facial features against stored templates.
    • Voice Recognition Service: Employs Librosa and TensorFlow to analyze voice samples, extracting unique features for verification against stored voice prints.
  • User Management Service: Manages user accounts, permissions, and session management.

4. Data Storage Layer

  • Biometric Database: Stores encrypted biometric templates (face and voice prints) in PostgreSQL, ensuring high security and fast access.
  • User Database: Maintains user profiles, authentication logs, and session data, facilitating efficient user management and audit trails.

5. Infrastructure Layer

  • Cloud Infrastructure (AWS/GCP): Hosts the application, providing scalable compute resources, storage, and networking capabilities.
  • Containerization (Docker): Packages the application and its dependencies into containers for consistent deployment across all environments.
  • Orchestration (Kubernetes): Manages the containers, ensuring they are running, scaled, and updated as needed.

6. Security and Compliance

  • Encryption: Applies SSL/TLS for data in transit and AES for data at rest, protecting sensitive information.
  • Authentication and Authorization: Implements JWT and OAuth 2.0 for secure access control and identity verification.
  • Compliance Tools: Automated tools to monitor and enforce GDPR, CCPA, and other regulatory standards.

7. Monitoring and Operations

  • Logging and Monitoring: Utilizes tools like Sentry and New Relic for real-time monitoring, logging, and alerting to ensure system health and performance.
  • CI/CD Pipeline: Integrates Git, Jenkins, or GitHub Actions for continuous integration and delivery, facilitating rapid development and deployment cycles.