Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection

TL;DR

This paper introduces a novel face presentation attack detection method that leverages partial attack supervision and regional weighted inference to improve accuracy on masked face datasets, adaptable to various neural network architectures.

Contribution

It proposes a flexible approach combining partial attack labels and regional focus to enhance masked face PAD, outperforming existing methods on the CRMA database.

Findings

Outperforms established PAD methods on CRMA database

Reduces false classifications of masked bona fide and partial attacks

Demonstrates benefits of partial supervision and regional weighting

Abstract

Wearing a mask has proven to be one of the most effective ways to prevent the transmission of SARS-CoV-2 coronavirus. However, wearing a mask poses challenges for different face recognition tasks and raises concerns about the performance of masked face presentation detection (PAD). The main issues facing the mask face PAD are the wrongly classified bona fide masked faces and the wrongly classified partial attacks (covered by real masks). This work addresses these issues by proposing a method that considers partial attack labels to supervise the PAD model training, as well as regional weighted inference to further improve the PAD performance by varying the focus on different facial areas. Our proposed method is not directly linked to specific network architecture and thus can be directly incorporated into any common or custom-designed network. In our work, two neural networks (DeepPixBis and MixFaceNet) are selected as backbones. The experiments are demonstrated on the collaborative real mask attack (CRMA) database. Our proposed method outperforms established PAD methods in the CRMA database by reducing the mentioned shortcomings when facing masked faces. Moreover, we present a detailed step-wise ablation study pointing out the individual and joint benefits of the proposed concepts on the overall PAD performance.