Explainable face recognition (XFR) is the problem of explaining the matches returned by a
facial matcher, in order to provide insight into why a probe was matched with one identity …

The application of artificial intelligence (AI) system is more and more extensive, using the
explainable AI (XAI) technology to explain why machine learning (ML) models make certain …

Given a facial matcher, in explainable face verification, the task is to answer: how relevant
are the parts of a probe image to establish the matching with an enrolled image. In many …

When explaining a recognition approach that can be used in facial analysis, eg, face
verification, face detection, attribute recognition, etc., the task is to answer: how relevant are …

While acoustic analysis methods have become a commodity in voice emotion research,
experiments that attempt not only to describe but to computationally manipulate expressive …

Current cognitive theories are cast in terms of information-processing mechanisms that use
mental representations,,–. For example, people use their mental representations to identify …

Deep neural networks (DNNs) can resolve real-world categorization tasks with apparent
human-level performance. However, true equivalence of behavioral performance between …

We propose methods to strengthen the invariance properties of representations obtained by
contrastive learning. While existing approaches implicitly induce a degree of invariance as …

The investigation of visual categorization has recently been aided by the introduction of
deep convolutional neural networks (CNNs), which achieve unprecedented accuracy in …

With impressive advances made in face recognition, the explainability has attracted more
and more attentions in the community, which delves into traceable and well-founded clues …