Search Results for author:
Found 6 papers, 1 papers with code
Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model
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 population (e. g. gender, ethnicity).
Ranked #1 on
Face Verification
on LFW
Learning an Ethical Module for Bias Mitigation of pre-trained Models
In spite of the high performance and reliability of deep learning algorithms in broad range everyday applications, many investigations tend to show that a lot of models exhibit biases, discriminating against some subgroups of the population.
An Assessment of GANs for Identity-related Applications
Generative Adversarial Networks (GANs) are now capable of producing synthetic face images of exceptionally high visual quality.
Robustness of Facial Recognition to GAN-based Face-morphing Attacks
Face-morphing attacks have been a cause for concern for a number of years.
von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification
Results show the effectiveness and excellent generalization ability of the proposed approach as it achieves state-of-the-art results on the LFW, YouTube faces and CACD datasets and competitive results on the IJB-A dataset.
DeepVisage: Making face recognition simple yet with powerful generalization skills
Although CNNs are mostly trained by optimizing the softmax loss, the recent trend shows an improvement of accuracy with different strategies, such as task-specific CNN learning with different loss functions, fine-tuning on target dataset, metric learning and concatenating features from multiple CNNs.
Ranked #6 on
Age-Invariant Face Recognition
on CACDVS
