Stéphane Gentric
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View article: Dynamic Autoencoders Against Adversarial Attacks
Dynamic Autoencoders Against Adversarial Attacks Open
International audience
View article: Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model
Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model Open
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 biases, discriminating against specific subgroups of the p…
View article: Robustness of Facial Recognition to GAN-based Face-morphing Attacks
Robustness of Facial Recognition to GAN-based Face-morphing Attacks Open
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 and detecting morphed images. These detection methods, howev…
View article: An Assessment of GANs for Identity-related Applications
An Assessment of GANs for Identity-related Applications Open
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, efforts have been made to develop metrics to objectively ass…
View article: von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification
von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification Open
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 distance can be simply computed by their angle. In this paper, we …
View article: DeepVisage: Making face recognition simple yet with powerful generalization skills
DeepVisage: Making face recognition simple yet with powerful generalization skills Open
Face recognition (FR) methods report significant performance by adopting the convolutional neural network (CNN) based learning methods. Although CNNs are mostly trained by optimizing the softmax loss, the recent trend shows an improvement …
View article: Similarity Function Learning with Data Uncertainty
Similarity Function Learning with Data Uncertainty Open
Similarity functions are at the core of many pattern recognition applications. Standard approaches use feature vectors extracted from a pair of images to compute their degree of similarity. Often feature vectors are noisy and a direct appl…