Self-restrained triplet loss for accurate masked face recognition. (April 2022)

• Record Type: Journal Article
• Authors: Boutros, Fadi; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan
• Is Part Of: Pattern recognition. Volume 124(2022)
• Publication Date: 2022-04
• Subjects: COVID-19 -- Biometric recognition -- Identity verification -- Masked face recognition; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.108473
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A solution to improve masked face verification performance. A novel loss function, the self-restrained triplet loss. Detailed evaluation on two real masked face datasets and two synthetically generated masked face datasets. Demonstration of the proposed solution on top of three face recognition models. Abstract: 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.