Jens-Uwe Garbas

TL;DR: This study compared data from physiological sensors and visual sensors for the detection of four types of distractions and identified problems, such as label jitter, scenario overfitting and unsatisfactory generalization performance, that may adversely affect related ML approaches.

TL;DR: This paper surveys the literature published in this field over the past decade, categorizes it, and identifies future research directions, and covers the pain datasets used in the reviewed literature, the learning tasks targeted by the approaches, the features extracted from images and image sequences to represent pain-related information, and the machine learning methods used.

TL;DR: The aim of this paper is to investigate the explainable AI methods Layer-wise Relevance Propagation (LRP) and Local Interpretable Model-agnostic Explanations (LIME), applied to explain how a deep neural network distinguishes facial expressions of pain from facial expression of emotions such as happiness and disgust.

TL;DR: In this paper, a global predictor and a local predictor are used in combination, where the global predictor derives a global tone-mapping function based on a statistical analysis of pairs of values of co-located samples in the first tonemapped version and the second version of the picture.

TL;DR: A framework is presented that utilizes two cameras to realize a spatial exposure bracketing, for which the different exposures are distributed among the cameras, and which enables the use of more complex camera setups with different sensors and provides robust camera responses.