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20222024
most citedMorDeephy: Face Morphing Detection Via Fused Classification

1 citations · 1 across the 6 of their papers we have counts for

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6 papers

cs.CV2024

Quadruplet Loss For Improving the Robustness to Face Morphing Attacks

Iurii Medvedev, Nuno Gonçalves

Recent advancements in deep learning have revolutionized technology and security measures, necessitating robust identification methods. Biometric approaches, leveraging personalize…

cs.CV2023

Fused Classification For Differential Face Morphing Detection

Iurii Medvedev, Joana Pimenta, Nuno Gonçalves

Face morphing, a sophisticated presentation attack technique, poses significant security risks to face recognition systems. Traditional methods struggle to detect morphing attacks,…

cs.CV2023

Impact of Image Context for Single Deep Learning Face Morphing Attack Detection

Joana Pimenta, Iurii Medvedev, Nuno Gonçalves

The increase in security concerns due to technological advancements has led to the popularity of biometric approaches that utilize physiological or behavioral characteristics for e…

cs.CV2023

EFaR 2023: Efficient Face Recognition Competition

Jan Niklas Kolf, Fadi Boutros, Jurek Elliesen +24

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition rece…

cs.CV2023

Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces Recognition

Iurii Medvedev, Farhad Shadmand, Nuno Gonçalves

Face recognition has achieved outstanding performance in the last decade with the development of deep learning techniques. Nowadays, the challenges in face recognition are related…

cs.CV20221 cited

MorDeephy: Face Morphing Detection Via Fused Classification

Iurii Medvedev, Farhad Shadmand, Nuno Gonçalves

Face morphing attack detection (MAD) is one of the most challenging tasks in the field of face recognition nowadays. In this work, we introduce a novel deep learning strategy for a…