Federated Face Presentation Attack Detection

TL;DR

This paper introduces FedPAD, a federated learning framework for face presentation attack detection that enables multiple data owners to collaboratively train a robust model without sharing private data.

Contribution

It proposes a novel federated learning approach for face PAD, addressing privacy concerns and enabling collaborative model training across multiple data sources.

Findings

FedPAD achieves effective global fPAD performance without data sharing.

The framework demonstrates robustness across different attack types.

Extensive experiments validate the effectiveness of federated learning in face PAD.

Abstract

Face presentation attack detection (fPAD) plays a critical role in the modern face recognition pipeline. A face presentation attack detection model with good generalization can be obtained when it is trained with face images from different input distributions and different types of spoof attacks. In reality, training data (both real face images and spoof images) are not directly shared between data owners due to legal and privacy issues. In this paper, with the motivation of circumventing this challenge, we propose Federated Face Presentation Attack Detection (FedPAD) framework. FedPAD simultaneously takes advantage of rich fPAD information available at different data owners while preserving data privacy. In the proposed framework, each data owner (referred to as \textit{data centers}) locally trains its own fPAD model. A server learns a global fPAD model by iteratively aggregating model updates from all data centers without accessing private data in each of them. Once the learned global model converges, it is used for fPAD inference. We introduce the experimental setting to evaluate the proposed FedPAD framework and carry out extensive experiments to provide various insights about federated learning for fPAD.