61 citations · 87 across the 5 of their papers we have counts for
5 papers
Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model
Jean-Rémy Conti, Nathan Noiry, Vincent Despiegel +2
In spite of the high performance and reliability of deep learning algorithms in a wide range of everyday applications, many investigations tend to show that a lot of models exhibit…
An Assessment of GANs for Identity-related Applications
Richard T. Marriott, Safa Madiouni, Sami Romdhani +2
Generative Adversarial Networks (GANs) are now capable of producing synthetic face images of exceptionally high visual quality. In parallel to the development of GANs themselves, e…
Robustness of Facial Recognition to GAN-based Face-morphing Attacks
Richard T. Marriott, Sami Romdhani, Stéphane Gentric +1
Face-morphing attacks have been a cause for concern for a number of years. Striving to remain one step ahead of attackers, researchers have proposed many methods of both creating a…
von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification
Md. Abul Hasnat, Julien Bohné, Jonathan Milgram +2
A number of pattern recognition tasks, \textit{e.g.}, face verification, can be boiled down to classification or clustering of unit length directional feature vectors whose distanc…
DeepVisage: Making face recognition simple yet with powerful generalization skills
Abul Hasnat, Julien Bohné, Jonathan Milgram +2
Face recognition (FR) methods report significant performance by adopting the convolutional neural network (CNN) based learning methods. Although CNNs are mostly trained by optimizi…