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On Recognizing Occluded Faces in the Wild 

BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group Erakın, Mustafa Ekrem; Demir, Uğur; Ekenel, Hazım Kemal
Facial appearance variations due to occlusion has been one of the main challenges for face recognition systems. To facilitate further research in this area, it is necessary and important to have occluded face datasets collected from real-world, as synthetically generated occluded faces cannot represent the nature of the ...

My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition 

BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group Neto, Pedro; Boutros, Fadi; Pinto, João Ribeiro; Saffari, Mohsen; Damer, Naser; Sequeira, Ana F.; Cardoso, Jaime S.
The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS). In this work, we address the challenge of masked face recognition (MFR) and focus on evaluating the verification performance in FRS when verifying ...

On Brightness Agnostic Adversarial Examples Against Face Recognition Systems 

BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group Singh, Inderjeet; Momiyama, Satoru; Kakizaki, Kazuya; Araki, Toshinori
This paper introduces a novel adversarial example generation method against face recognition systems (FRSs). An adversarial example (AX) is an image with deliberately crafted noise to cause incorrect predictions by a target system. The AXs generated from our method remain robust under real-world brightness changes. Our ...

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Author

Araki, Toshinori (1)Boutros, Fadi (1)Cardoso, Jaime S. (1)Damer, Naser (1)Demir, Uğur (1)Ekenel, Hazım Kemal (1)Erakın, Mustafa Ekrem (1)Kakizaki, Kazuya (1)Momiyama, Satoru (1)Neto, Pedro (1)... View More

Subject

Face recognition (3)
Adversarial examples (1)Brightness variations (1)Covid-19 (1)Curriculum learning (1)deep learning (1)face occlusion (1)masked face recognition (1)real-world occluded faces (1)triplet loss (1)... View More

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2021 (3)

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Yes (3)

About uns | FAQ | Help | Imprint | Datenschutz

Gesellschaft für Informatik e.V. (GI), Kontakt: Geschäftsstelle der GI
Diese Digital Library basiert auf DSpace.