MultiModelFaceAntiSpoof

Multiple Open Source Spoofing models to Inference via Flask API

MultiModelFaceAntiSpoof

An advanced system utilizing multiple open source spoofing detection models to create a robust face anti-spoofing solution. This project implements a Flask API for easy integration with existing authentication systems.

Architecture

The solution aggregates results from various state-of-the-art face anti-spoofing models to achieve higher accuracy and resilience against different spoofing techniques.

High-level architecture showing how multiple models are combined for robust face anti-spoofing detection.

Technical Details

  • Models: Combination of PyTorch and TensorFlow models for diverse approach detection
  • API: Flask-based RESTful API with Swagger documentation
  • Containerization: Docker for easy deployment and scalability
  • Performance: Optimized inference for real-time applications
  • Web Server: Nginx for efficient request handling and load balancing

Implementation Challenges

Developing this system involved addressing several key challenges:

  1. Optimizing different model architectures for consistent inference speed
  2. Creating a unified confidence scoring system across varied model outputs
  3. Ensuring the API could handle high-throughput scenarios
  4. Balancing accuracy with performance for real-time applications
Demonstration of the system detecting various spoofing attempts including printed photos, digital screens, and 3D masks.