Surveillance Face Presentation Attack Detection Challenge

• URL: http://arxiv.org/abs/2304.07580v1
• Date: Sat, 15 Apr 2023 15:23:19 GMT
• Title: Surveillance Face Presentation Attack Detection Challenge
• Authors: Hao Fang, Ajian Liu, Jun Wan, Sergio Escalera, Hugo Jair Escalante, Zhen Lei
• Abstract summary: Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. We collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) SuHiFiMask contains $10,195$ videos from $101$ subjects of different age groups, which are collected by $7$ mainstream surveillance cameras. We organize a face presentation attack detection challenge in surveillance scenarios.
• Score: 68.06719263243806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, most of the studies lacked consideration of long-distance scenarios. Specifically, compared with FAS in traditional scenes such as phone unlocking, face payment, and self-service security inspection, FAS in long-distance such as station squares, parks, and self-service supermarkets are equally important, but it has not been sufficiently explored yet. In order to fill this gap in the FAS community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask). SuHiFiMask contains $10,195$ videos from $101$ subjects of different age groups, which are collected by $7$ mainstream surveillance cameras. Based on this dataset and protocol-$3$ for evaluating the robustness of the algorithm under quality changes, we organized a face presentation attack detection challenge in surveillance scenarios. It attracted 180 teams for the development phase with a total of 37 teams qualifying for the final round. The organization team re-verified and re-ran the submitted code and used the results as the final ranking. In this paper, we present an overview of the challenge, including an introduction to the dataset used, the definition of the protocol, the evaluation metrics, and the announcement of the competition results. Finally, we present the top-ranked algorithms and the research ideas provided by the competition for attack detection in long-range surveillance scenarios.

Related papers

• Unified Physical-Digital Attack Detection Challenge [70.67222784932528]
  Face Anti-Spoofing (FAS) is crucial to safeguard Face Recognition (FR) Systems. UniAttackData is the largest public dataset for Unified Attack Detection. We organized a Unified Physical-Digital Face Attack Detection Challenge to boost the research in Unified Attack Detections.
  arXiv  Detail & Related papers  (2024-04-09T11:00:11Z)
• Surveillance Face Anti-spoofing [81.50018853811895]
  Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. We propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality. A large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.
  arXiv  Detail & Related papers  (2023-01-03T07:09:57Z)
• Masked Face Recognition Challenge: The InsightFace Track Report [79.77020394722788]
  During the COVID-19 coronavirus epidemic, almost everyone wears a facial mask, which poses a huge challenge to deep face recognition. In this workshop, we focus on bench-marking deep face recognition methods under the existence of facial masks.
  arXiv  Detail & Related papers  (2021-08-18T15:14:44Z)
• 3D High-Fidelity Mask Face Presentation Attack Detection Challenge [79.2407530090659]
  A large-scale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask has been collected. We organized a 3D High-Fidelity Mask Face Presentation Attack Detection Challenge to boost the research of 3D mask-based attack detection.
  arXiv  Detail & Related papers  (2021-08-16T08:40:12Z)
• Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review [79.49390241265337]
  Chalearn Face Anti-spoofing Attack Detection Challenge consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results.
  arXiv  Detail & Related papers  (2020-04-23T06:43:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.