Boutros, Fadi ; Damer, Naser ; Kirchbuchner, Florian ; Kuijper, Arjan (2022)
Self-restrained triplet loss for accurate masked face recognition.
In: Pattern Recognition, 124
doi: 10.1016/j.patcog.2021.108473
Artikel, Bibliographie
Kurzbeschreibung (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.
Typ des Eintrags: | Artikel |
---|---|
Erschienen: | 2022 |
Autor(en): | Boutros, Fadi ; Damer, Naser ; Kirchbuchner, Florian ; Kuijper, Arjan |
Art des Eintrags: | Bibliographie |
Titel: | Self-restrained triplet loss for accurate masked face recognition |
Sprache: | Englisch |
Publikationsjahr: | April 2022 |
Verlag: | Elsevier |
Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Pattern Recognition |
Jahrgang/Volume einer Zeitschrift: | 124 |
DOI: | 10.1016/j.patcog.2021.108473 |
Kurzbeschreibung (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. |
Freie Schlagworte: | Biometrics, Face recognition, Deep learning, Machine learning |
Zusätzliche Informationen: | Art.No.: 108473 |
Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
Hinterlegungsdatum: | 16 Feb 2022 08:21 |
Letzte Änderung: | 16 Feb 2022 08:21 |
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