Search

This paper describes the 9th Emotion Recognition in the Wild (EmotiW) challenge, which is being run as a grand challenge at the 25th ACM International Conference on Multimodal Interaction 2023. EmotiW challenge focuses on affect related benchmarking tasks and comprises of two sub-challenges: a) User Engagement Prediction in the Wild, and b) Audio-Visual Group-based Emotion Recognition. The purpose of this challenge is to provide a common platform for researchers from diverse domains. The objective is to promote the development and assessment of methods, which can predict engagement levels and/or identify perceived emotional well-being of a group of individuals in real-world circumstances. We describe the datasets, the challenge protocols and the accompanying sub-challenge.

The two-volume set CCIS 1142 and 1143 constitutes thoroughly refereed contributions presented at the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019. For ICONIP 2019 a total of 345 papers was carefully reviewed and selected for publication out of 645 submissions. The 168 papers included in this volume set were organized in topical sections as follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred computing; human centred computing and medicine; human centred computing for emotion; hybrid models; image processing by neural techniques; learning from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based approaches; social network computing; spiking neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models.

This paper introduces the Eighth Emotion Recognition in the Wild (EmotiW) challenge. EmotiW is a benchmarking effort run as a grand challenge of the 22nd ACM International Conference on Multimodal Interaction 2020. It comprises of four tasks related to automatic human behavior analysis: a) driver gaze prediction; b) audio-visual group-level emotion recognition; c) engagement prediction in the wild; and d) physiological signal based emotion recognition. The motivation of EmotiW is to bring researchers in affective computing, computer vision, speech processing and machine learning to a common platform for evaluating techniques on a test data. We discuss the challenge protocols, databases and their associated baselines.

This paper describes the Seventh Emotion Recognition in the Wild (EmotiW) Challenge. The EmotiW benchmarking platform provides researchers with an opportunity to evaluate their methods on affect labelled data. This year EmotiW 2019 encompasses three sub-challenges: a) Group-level cohesion prediction; b) Audio-Video emotion recognition; and c) Student engagement prediction. We discuss the databases used, the experimental protocols and the baselines.

An important factor in the operational success of any teleoperated human-swarm system is situation awareness (SA). A loss of SA has been associated with poor human performance, which can lead to misjudgement, errors, and life-threatening situations. One of the major factors that causes loss of SA is the degradation of data transmission. It is imperative to assess the SA of an operator before the performance of a teleoperated system has declined, in particular in situations of delayed relay and/or loss of critical information. We use electroencephalography (EEG) to predict different levels of SA. A human-swarm simulation was used to obtain subjective scores from participants. Quality of information significantly affected the perception of SA of the participants. EEG data provided objective confirmation of the resultant SA level. Theta, Alpha, and Beta band exhibited an increase during loss of SA. Frontal and occipital areas were identified to reflect changes in SA. These preliminary results offer evidence for the potential use of EEG to offer real-time indicators for the objective assessment of SA.

We analyse a large drug prescription dataset and test the hypothesis that drug prescription data can be used to predict further complications in older patients newly diagnosed with type 2 diabetes mellitus. More specifically, we focus on mortality and polypharmacy prediction. We also examine the balance between interpretability and predictive performance for both prediction tasks, and compare performance of interpretable models with models generated with automated methods. Our results show good predictive performance in the polypharmacy prediction task with AUC of 0.859 (95% CI: 0.857–0.861). On the other hand, we were only able to achieve the average predictive performance for mortality prediction task with AUC of 0.754 (0.747–0.761). It was also shown that adding additional drug related features increased the performance only in the polypharmacy prediction task, while additional information on prescribed drugs did not influence the performance in the mortality prediction. Despite the limited success in mortality prediction, this study demonstrates the added value of the systematic collection and use of Electronic Health Record (EHR) data in solving the problem of polypharmacy related complications in older age.

Research in automatic affect recognition has come a long way. This paper describes the fifth Emotion Recognition in the Wild (EmotiW) challenge 2017. EmotiW aims at providing a common benchmarking platform for researchers working on different aspects of affective computing. This year there are two sub-challenges: A) Audio-video emotion recognition and b) group-level emotion recognition. These challenges are based on the acted facial expressions in the wild and group affect databases, respectively. The particular focus of the challenge is to evaluate method in 'in the wild' settings. 'In the wild' here is used to describe the various environments represented in the images and videos, which represent real-world (not lab like) scenarios. The baseline, data, protocol of the two challenges and the challenge participation are discussed in detail in this paper.