Abstract

During the COVID-19 coronavirus epidemic, almost everyone wears a facial mask, which poses a huge challenge to face recognition. Traditional face recognition systems may not effectively recognize the masked faces, but removing the mask for authentication will increase the risk of virus infection. Inspired by the COVID-19 pandemic response, the widespread requirement that people wear protective face masks in public places has driven a need to understand how face recognition technology deals with occluded faces, often with just the periocular area and above visible.

To cope with the challenge arising from wearing masks, it is crucial to improve the existing face recognition approaches. Recently, some commercial providers have announced the availability of face recognition algorithms capable of handling face masks, and an increasing number of research publications have surfaced on the topic of face recognition on people wearing masks. However, due to the sudden outbreak of the epidemic, there is yet no publicly available masked face recognition benchmark. In this workshop, we will organise Masked Face Recognition (MFR) challenge and focus on bench-marking deep face recognition methods under the existence of facial masks.

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Links

Workshop Homepage: masked-face-recognition-challenge

Github Homepage including submission rules: iccv21-mfr

There're InsightFace track here and WebFace track in this workshop.

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Workshop Agenda

Monday, October 11, 2021, 7:00 AM - 6:00 PM (eastern time zone)

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Invited Talks

Patrick Grother: Deep Insight on Face Recognition Vendor Test

Xiaoming Liu: Trustworthy Face Recognition

Rama Chellappa: Fair Face Recognition

Jianshu Li: Face Recognition Under Financial Scenarios

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