Related papers: The BIRAFFE2 Experiment. Study in Bio-Reactions an…
The field of artificial intelligence has a strong interest in the topic of emotion recognition. The majority of extant emotion recognition models are oriented towards enhancing the precision of discrete emotion label prediction. Given the…
Emotions recognition is commonly employed for health assessment. However, the typical metric for evaluation in therapy is based on patient-doctor appraisal. This process can fall into the issue of subjectivity, while also requiring…
The quantified measurement of facial expressiveness is crucial to analyze human affective behavior at scale. Unfortunately, methods for expressiveness quantification at the video frame-level are largely unexplored, unlike the study of…
Human emotions analysis has been the focus of many studies, especially in the field of Affective Computing, and is important for many applications, e.g. human-computer intelligent interaction, stress analysis, interactive games, animations,…
Emotional Artificial Intelligences are currently one of the most anticipated developments of AI. If successful, these AIs will be classified as one of the most complex, intelligent nonhuman entities as they will possess sentience, the…
Emotion Classification based on text is a task with many applications which has received growing interest in recent years. This paper presents a preliminary study with the goal to help researchers and practitioners gain insight into…
There is an increasing consensus among re- searchers that making a computer emotionally intelligent with the ability to decode human affective states would allow a more meaningful and natural way of human-computer interactions (HCIs). One…
This paper proposes a process for a classification model for the facial expressions. The proposed process would aid in specific categorisation of children's emotions from 2 emotions namely 'Happy' and 'Sad'. Since the existing emotion…
This paper discusses a fuzzy model for multi-level human emotions recognition by computer systems through keyboard keystrokes, mouse and touchscreen interactions. This model can also be used to detect the other possible emotions at the time…
Accurate recognition of human emotions is critical for adaptive human-computer interaction, yet remains challenging in dynamic, conversation-like settings. This work presents a personality-aware multimodal framework that integrates…
Understanding complex user behaviour under various conditions, scenarios and journeys can be fundamental to the improvement of the user-experience for a given system. Predictive models of user reactions, responses -- and in particular,…
Affective Behavior Analysis aims to facilitate technology emotionally smart, creating a world where devices can understand and react to our emotions as humans do. To comprehensively evaluate the authenticity and applicability of emotional…
Multimodal emotion analysis performed better in emotion recognition depending on more comprehensive emotional clues and multimodal emotion dataset. In this paper, we developed a large multimodal emotion dataset, named "HED" dataset, to…
Emotion recognition in social situations is a complex task that requires integrating information from both facial expressions and the situational context. While traditional approaches to automatic emotion recognition have focused on…
This paper explores the growing presence of emotionally responsive artificial intelligence through a critical and interdisciplinary lens. Bringing together the voices of early-career researchers from multiple fields, it explores how AI…
Emotions are best way of communicating information; and sometimes it carry more information than words. Recently, there has been a huge interest in automatic recognition of human emotion because of its wide spread application in security,…
Understanding emotions is fundamental to human interaction and experience. Humans easily infer emotions from situations or facial expressions, situations from emotions, and do a variety of other affective cognition. How adept is modern AI…
Affective computing has been largely limited in terms of available data resources. The need to collect and annotate diverse in-the-wild datasets has become apparent with the rise of deep learning models, as the default approach to address…
The integration of emotional intelligence in machines is an important step in advancing human-computer interaction. This demands the development of reliable end-to-end emotion recognition systems. However, the scarcity of public affective…
Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly. We argue that empathetic responses often mimic the emotion of the…