期刊
PATTERN RECOGNITION
卷 124, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2021.108473
关键词
COVID-19; Biometric recognition; Identity verification; Masked face recognition
资金
- German Federal Ministry of Education and Research
- Hessen State Ministry for Higher Education, Research and the Arts within National Research Center for Applied Cybersecurity ATHENE
This paper presents a solution to improve masked face recognition performance by introducing the Embedding Unmasking Model (EUM) and the Self-restrained Triplet (SRT) loss function. Experimental results demonstrate that the proposed approach significantly enhances performance in various settings.
Using the face as a biometric identity trait is motivated by the contactless nature of the capture process and the high accuracy of the recognition algorithms. After the current COVID-19 pandemic, wearing a face mask has been imposed in public places to keep the pandemic under control. However, face occlusion due to wearing a mask presents an emerging challenge for face recognition systems. In this paper, we present a solution to improve masked face recognition performance. Specifically, we propose the Embedding Unmasking Model (EUM) operated on top of existing face recognition models. We also propose a novel loss function, the Self-restrained Triplet (SRT), which enabled the EUM to produce embeddings similar to these of unmasked faces of the same identities. The achieved evaluation results on three face recognition models, two real masked datasets, and two synthetically generated masked face datasets proved that our proposed approach significantly improves the performance in most experimental settings. (c) 2021 Elsevier Ltd. All rights reserved.
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