Synthetic data is gaining increasing relevance for training machine learning models. This is
mainly motivated due to several factors such as the lack of real data and intra-class …

Over the last decade, the use of Automatic Speaker Verification (ASV) systems has become
increasingly widespread in response to the growing need for secure and efficient identity …

The features learned by deep-learning based face recognition networks pose privacy risks
as they encode sensitive information that could be used to infer demographic attributes. In …

Deep learning-based face recognition systems produce templates that encode sensitive
information next to identity, such as gender and ethnicity. This poses legal and ethical …

Modern face recognition systems utilize deep neural networks to extract salient features from
a face. These features denote embeddings in latent space and are often stored as templates …

Soft-biometric privacy enhancing techniques (SB-PETs) transform facial images to preserve
identity while preventing the automatic extraction of soft-biometrics by confusing machines …

Following the success of soft biometrics over traditional biometrics, anthropometric soft
biometrics are emerging as candidate features for recognition or retrieval using an …

Machine Learning (ML) can be broadly defined as empowering machines with large
datasets to analyze and make informed decisions. As ML is integrated into real-world …

The increasing number of mobile devices in recent years has caused the collection of a
large amount of personal information that needs to be protected. To this aim, be-havioural …

State-of-the-art face recognition models commonly extract information-rich biometric
templates from the input images that are then used for comparison purposes and identity …