3D High-Fidelity Mask Face Presentation Attack Detection Challenge

TL;DR

This paper introduces a large-scale dataset and a challenge for detecting 3D mask-based face presentation attacks, aiming to improve algorithms' discrimination and generalization in real-world scenarios.

Contribution

It provides a new high-fidelity mask dataset, defines a standardized protocol, and organizes a challenge that fosters research and development in mask attack detection.

Findings

Top algorithms show improved detection accuracy.

The dataset enables robust training and evaluation.

The challenge results highlight key research directions.

Abstract

The threat of 3D masks to face recognition systems is increasingly serious and has been widely concerned by researchers. To facilitate the study of the algorithms, a large-scale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask) has been collected. Specifically, it consists of a total amount of 54, 600 videos which are recorded from 75 subjects with 225 realistic masks under 7 new kinds of sensors. Based on this dataset and Protocol 3 which evaluates both the discrimination and generalization ability of the algorithm under the open set scenarios, we organized a 3D High-Fidelity Mask Face Presentation Attack Detection Challenge to boost the research of 3D mask-based attack detection. It attracted 195 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-run by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including the introduction of the dataset used, the definition of the protocol, the calculation of the evaluation criteria, and the summary and publication of the competition results. Finally, we focus on introducing and analyzing the top ranking algorithms, the conclusion summary, and the research ideas for mask attack detection provided by this competition.