Fang, Meiling ; Boutros, Fadi ; Kuijper, Arjan ; Damer, Naser (2021)
Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection.
16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021). virtual Conference (15.-18.12.2021)
doi: 10.1109/FG52635.2021.9667051
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Wearing a mask has proven to be one of the most effective ways to prevent the transmission of SARS-Co V-2 coronavirus. However, wearing a mask poses challenges for different face recognition tasks and raises concerns about the performance of masked face presentation detection (PAD). The main issues facing the mask face PAD are the wrongly classified bona fide masked faces and the wrongly classified partial attacks (covered by real masks). This work addresses these issues by proposing a method that considers partial attack labels to supervise the PAD model training, as well as regional weighted inference to further improve the PAD performance by varying the focus on different facial areas. Our proposed method is not directly linked to specific network architecture and thus can be directly incorporated into any common or custom-designed network. In our work, two neural networks (DeepPixBis [21] and MixFaceNet [4]) are selected as backbones. The experiments are demonstrated on the collaborative real mask attack (CRMA) database [17]. Our proposed method outperforms established PAD methods in the CRMA database by reducing the mentioned shortcomings when facing masked faces. Moreover, we present a detailed step-wise ablation study pointing out the individual and joint benefits of the proposed concepts on the overall PAD performance.
Typ des Eintrags: | Konferenzveröffentlichung |
---|---|
Erschienen: | 2021 |
Autor(en): | Fang, Meiling ; Boutros, Fadi ; Kuijper, Arjan ; Damer, Naser |
Art des Eintrags: | Bibliographie |
Titel: | Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection |
Sprache: | Englisch |
Publikationsjahr: | 2021 |
Verlag: | IEEE |
Buchtitel: | Proceedings: 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021) |
Veranstaltungstitel: | 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021) |
Veranstaltungsort: | virtual Conference |
Veranstaltungsdatum: | 15.-18.12.2021 |
DOI: | 10.1109/FG52635.2021.9667051 |
Kurzbeschreibung (Abstract): | Wearing a mask has proven to be one of the most effective ways to prevent the transmission of SARS-Co V-2 coronavirus. However, wearing a mask poses challenges for different face recognition tasks and raises concerns about the performance of masked face presentation detection (PAD). The main issues facing the mask face PAD are the wrongly classified bona fide masked faces and the wrongly classified partial attacks (covered by real masks). This work addresses these issues by proposing a method that considers partial attack labels to supervise the PAD model training, as well as regional weighted inference to further improve the PAD performance by varying the focus on different facial areas. Our proposed method is not directly linked to specific network architecture and thus can be directly incorporated into any common or custom-designed network. In our work, two neural networks (DeepPixBis [21] and MixFaceNet [4]) are selected as backbones. The experiments are demonstrated on the collaborative real mask attack (CRMA) database [17]. Our proposed method outperforms established PAD methods in the CRMA database by reducing the mentioned shortcomings when facing masked faces. Moreover, we present a detailed step-wise ablation study pointing out the individual and joint benefits of the proposed concepts on the overall PAD performance. |
Freie Schlagworte: | Face recognition, Spoofing attacks, Machine learning, Deep learning, Biometrics |
Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
Hinterlegungsdatum: | 03 Mär 2022 08:47 |
Letzte Änderung: | 03 Mär 2022 08:47 |
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