This paper presents a comprehensive review of Automatic Facial Recognition Systems
using integrative and systematic mapping approach. The review is grounded on criteria …

We explore varying face recognition accuracy across demographic groups as a
phenomenon partly caused by differences in face illumination. We observe that for a …

Biometric systems are largely based on Machine Learning (ML) algorithms which are often
considered as a black-box. There is a need to provide them with explanations to make their …

Abstract Explainable Face Recognition is gaining growing attention as the use of the
technology is gaining ground in security-critical applications. Understanding why two face …

Systems capable of analyzing and quantifying human physical or behavioral traits, known as
biometrics systems, are growing in use and application variability. Since its evolution from …

Face recognition (FR) systems continue to spread in our daily lives with an increasing
demand for higher explain-ability and interpretability of FR systems that are mainly based on …

Face Super-Resolution (FSR) plays a crucial role in enhancing low-resolution face images,
which is essential for various face-related tasks. However, FSR may alter individuals' …

As two sides of the same coin, causality and explainable artificial intelligence (xAI) were
initially proposed and developed with different goals. However, the latter can only be …

A MasterFace is a face image that can successfully match against a large portion of the
population. Since their generation does not require access to the information of the enrolled …

The goal of the project" Facial Metrics for EES" is to develop, implement and publish an
open source algorithm for the quality assessment of facial images (OFIQ) for face …