Few-Shot Learning: Expanding ID Cards Presentation Attack Detection to Unknown ID Countries

Abstract

This paper proposes a Few-shot Learning (FSL) approach for detecting Presentation Attacks on ID Cards deployed in a remote verification system and its extension to new countries. Our research analyses the performance of Prototypical Networks across documents from Spain and Chile as a baseline and measures the extension of generalisation capabilities of new ID Card countries such as Argentina and Costa Rica. Specifically targeting the challenge of screen display presentation attacks. By leveraging convolutional architectures and meta-learning principles embodied in Prototypical Networks, we have crafted a model that demonstrates high efficacy with Few-shot examples. This research reveals that competitive performance can be achieved with as Few-shots as five unique identities and with under 100 images per new country added. This opens a new insight for novel generalised Presentation Attack Detection on ID cards to unknown attacks.

References in corpus (6)

• Prototypical Networks for Few-shot Learning
• MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream
• MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis
• MIDV-2019: Challenges of the modern mobile-based document OCR
• Sejong Face Database: A Multi-Modal Disguise Face Database
• Single Morphing Attack Detection using Siamese Network and Few-shot Learning