1 citations · 1 across the 5 of their papers we have counts for
7 papers
Massively Annotated Datasets for Assessment of Synthetic and Real Data in Face Recognition
Pedro C. Neto, Rafael M. Mamede, Carolina Albuquerque +2
Face recognition applications have grown in parallel with the size of datasets, complexity of deep learning models and computational power. However, while deep learning models evol…
Model Compression Techniques in Biometrics Applications: A Survey
Eduarda Caldeira, Pedro C. Neto, Marco Huber +2
The development of deep learning algorithms has extensively empowered humanity's task automatization capacity. However, the huge improvement in the performance of these models is h…
Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognition
Pedro C. Neto, Eduarda Caldeira, Jaime S. Cardoso +1
With the ever-growing complexity of deep learning models for face recognition, it becomes hard to deploy these systems in real life. Researchers have two options: 1) use smaller mo…
Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection
Eduarda Caldeira, Pedro C. Neto, Tiago Gonçalves +3
Morphing attacks keep threatening biometric systems, especially face recognition systems. Over time they have become simpler to perform and more realistic, as such, the usage of de…
OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement
Pedro C. Neto, Tiago Gonçalves, Marco Huber +3
Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems. It consists of selecting two faces from different individuals and fusing t…
SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data
Marco Huber, Fadi Boutros, Anh Thi Luu +16
This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Con…