Projects
FACING2 – Face Image Understanding
The FACING-2 project aims to study and develop methods that allow exploring facial biometrics of hum...
Publications
Impact of Image Context for Single Deep Learning Face Morphing Attack Detection
The increase in security concerns due to techno- logical advancements has led to the popularity of biometric approaches that utilize physiological or behavioral characteris- tics for enhanced recognition. Face recognition systems (FRSs) have become prevalent, but they are still vulnerable to image manipulation techniques such as face morphing attacks. This study investigates the impact of the alignment settings of input images on deep learning face morphing detection performance. We analyze the interconnections between the face contour and image context and suggest optimal alignment conditions for face morphing detection.
- Date: 22/09/2023
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- Featured In: 2023 International Conference of the Biometrics Special Interest Group (BIOSIG)
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- Publication Type: Conference Papers
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- Author(s): Joana Alves Pimenta, Iurii Medvedev and Nuno Gonçalves
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- DOI: 10.1109/BIOSIG58226.2023.10345999
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Fused Classification for Differential Face Morphing Detection
Face morphing, a sophisticated presentation attack tech- nique, poses signi cant security risks to face recognition systems. Traditional methods struggle to detect morph- ing attacks, which involve blending multiple face images to create a synthetic image that can match different individ- uals. In this paper, we focus on the differential detection of face morphing and propose an extended approach based on fused classi cation method for no-reference scenario. We introduce a public face morphing detection benchmark for the differential scenario and utilize a speci c data min- ing technique to enhance the performance of our approach. Experimental results demonstrate the effectiveness of our method in detecting morphing attacks.
- Date: 04/01/2024
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- Featured In: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024)
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- Publication Type: Conference Papers
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- Author(s): Iurii Medvedev, Joana Alves Pimenta and Nuno Gonçalves
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- DOI: 10.48550/arXiv.2309.00665
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