Masked Face Recognition: Progress, Dataset, and Dataset Generation

被引：2

作者：

Utomo, Yesun ^{[1
]}

Kusuma, Gede Putra ^{[2
]}

机构：

[1] Bina Nusantara Univ, Comp Sci Dept, Sch Comp Sci, Jakarta 11480, Indonesia

[2] Bina Nusantara Univ, Comp Sci Dept, Binus Grad Program, Master Comp Sci, Jakarta 11480, Indonesia

来源：

3RD INTERNATIONAL CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS (ICORIS 2021) | 2021年

关键词：

Masked face recognition; mask face recognition dataset; mask face dataset generation;

D O I：

10.1109/ICORIS52787.2021.9649622

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Face recognition is one of the oldest and famous research in computer vision and has improved over the years. However, most research of this topic are designed to recognize full human face. Ever since COVID-19 pandemic, most people must wear face mask when going outside. This create new challenges for existing face recognition as it is proven to be ineffective recognizing people wearing mask. This paper focuses on discussing previous work of Masked Face Recognition, masked face recognition dataset that can be used for training, and dataset generation tools to generate new masked face dataset using existing face recognition dataset. Analysis suggests that cropping based approach is popular among researchers to solve masked face recognition combined with other method like triplet loss implemented in ResNet-50.

引用

页码：419 / 422

页数：4

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