Cross‐ethnicity face anti‐spoofing recognition challenge: A review

Abstract

Face anti‐spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has achieved impressive progress recently due to the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti‐spoofing. Recently, a multi‐ethnic face anti‐spoofing dataset, CASIA‐SURF cross‐ethnicity face anti‐spoofing (CeFA), has been released with the goal of measuring the ethnic bias. It is the largest up to date CeFA dataset covering three ethnicities, three modalities, 1607 subjects, 2D plus 3D attack types and the first dataset including explicit ethnic labels among the recently released datasets for face anti‐spoofing. We organized the Chalearn Face Anti‐spoofing Attack Detection Challenge which consists of single‐modal (e.g. RGB) and multi‐modal (e.g. RGB, Depth, infrared) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally, 11 and eight teams have submitted their codes in the single‐modal and multi‐modal face anti‐spoofing recognition challenges, respectively. All of the results were verified and re‐ran by the organizing team, and the results were used for the final ranking. This study presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyse the top‐ranked solutions and draw conclusions derived from the competition. Besides, we outline future work directions.

SACN: A Novel Rotating Face Detector Based on Architecture Search

“…Finally, we found that our dataset was not balanced in terms of race, which is a key point for face detection, as mentioned in [27,28]. The authors of [28] constructed a balanced race dataset, including White, Black, Indian, East Asian, Southeast Asian, Middle East, and Latino faces.…”

Revisiting Pixel-Wise Supervision for Face Anti-Spoofing

“…Ma et al [49] proposed a multi-regional CNN with the local binary classification loss to local patches. Yu et al [13] utilized pixel-wise binary label to supervise the multimodal CDCN and won the first place in the ChaLearn multi-modal face anti-spoofing attack detection challenge @CVPR2020 [74]. With the help of spatially positional knowledge, binary mask label not only boosts the models' discrimination, but also benefits neural architecture search.…”

Evaluating Methods of Anti-Spoofing of Living Entities and Propose Solutions

“…Алиа в [4] и Зун и др. в [5] придумали метод запроса пользователя на выполнение некоторой случайной инструкции, а затем проверенного ответа, чтобы подтвердить, были ли инструкции выполнены или нет.…”