IEEE Transactions on Affective Computing

We present a dataset for the analysis of human affective states using functional near-infrared spectroscopy (fNIRS). Data were recorded from thirty-one participants who engaged in two tasks. In the emotional perception task the participants passively ...

Stress, especially chronic stress, is a high risk factor of many physical and mental health problems. This work acquired 702 days of full-day ambulatory electrocardiogram (ECG) and Tri-axial acceleration (T-ACC) data from 104 healthy college students and ...

This article proposes a data-driven approach for generating personalized smile style images for neutral expressions, which aims to produce diverse smile styles while preserving individual features. Unlike other generator models that require expensive ...

Emotion elicitation using emotional film clips is one of the most common and ecologically valid methods in Affective Computing. However, selecting and validating appropriate materials that evoke a range of emotions is challenging. Here, we present EmoStim:...

The success of supervised models for emotion recognition on images heavily depends on the availability of images properly annotated. Although millions of images are presently available, only a few are annotated with reliable emotional information. Current ...

Emotions are important factors in decision-making. With the advent of brain-computer interface (BCI) techniques, researchers developed a strong interest in predicting decisions based on emotions, which is a challenging task. To predict decision-making ...

Emotion recognition in conversation (ERC) and emotional response generation (ERG) are two important NLP tasks. ERC aims to detect the utterance-level emotion from a dialogue, while ERG focuses on expressing a desired emotion. Essentially, ERC is a ...

Recent advances in artificial speech synthesis and machine learning equip AI-powered conversational agents, from voice assistants to social robots, with the ability to mimic human emotional expression during their interactions with users. One unexplored ...

Multimodal aspect-based sentiment analysis (MABSA) aims to determine the sentiment polarity of each aspect mentioned in the text based on multimodal content. Various approaches have been proposed to model multimodal sentiment features for each aspect via ...

Facial expression recognition (FER) attracts much attention in computer vision. Previous works mostly study the single-label FER problem. The more complex multi-label facial expression recognition task is underexplored. Multi-label FER is more challenging ...

Most existing multi-modal emotion recognition studies are targeted at a classification task that aims to assign a specific emotion category to a combination of several heterogeneous input data, including multimedia signals and physiological signals. A ...

Self-supervised learning has shown value for uncovering informative movement features for human activity recognition. However, there has been minimal exploration of this approach for affect recognition where availability of large labelled datasets is ...

Electroencephalogram (EEG) signals have been widely studied in human emotion recognition. The majority of existing EEG emotion recognition algorithms utilize dozens or hundreds of electrodes covering the whole scalp region (denoted as full-channel EEG ...

Olfactory-enhanced virtual reality (OVR) creates a complex and rich emotional experience, thus promoting a new generation of human-computer interaction experiences in real-world scenarios. However, with the rise of virtual reality (VR) as a mood induction ...

Micro-expression recognition (MER) holds significance in uncovering hidden emotions. Most works take image sequences as input and cannot effectively explore ME information because subtle ME-related motions are easily submerged in unrelated information. ...

Multi-modal emotion recognition has attracted increasing attention in human-computer interaction, as it extracts complementary information from physiological and behavioral features. Compared to single modal approaches, multi-modal fusion methods are more ...

One of the most important subconscious reactions, micro-expression (ME), is a spontaneous, subtle, and transient facial expression that reveals human beings’ genuine emotion. Therefore, automatically recognizing ME (MER) is becoming increasingly ...

Depressive episode is key symptom collection of mood disorders. Early intervention can prevent it from happening or reduce its impact, and close monitoring can greatly improve medical management. However, most current monitoring methods are ex post facto, ...

Recently audio-driven talking face video generation has attracted considerable attention. However, very few researches address the issue of emotional editing of these talking face videos with continuously controllable expressions, which is a strong demand ...

Aesthetic quality evaluation has been an important research question in the field of user experience in product design. However, the feasibility and accuracy of using fNIRS data for product aesthetic quality evaluation are unknown. In this article, we ...

Emotions play an essential role in affecting the performance of cognitive abilities in continuous cognitive tasks. Most previous studies share a common issue in that the evoked emotions are simply presumed to be real emotions, without taking into account ...

Recently, multi-modal affective computing has demonstrated that introducing multi-modal information can enhance performance. However, multi-modal research faces significant challenges due to its high requirements regarding data acquisition, modal ...

EEG emotion recognition has been hampered by the clear individual differences in the electroencephalogram (EEG). Nowadays, domain adaptation is a good way to deal with this issue because it aligns the distribution of data across subjects. However, the ...

Assigning lifelike qualities to robotic agents (Anthropomorphism) is associated with complex affective interpretations of their behavior. These anthropomorphized perceptions are traditionally elicited through robots’ designs. Yet, aerial robots (or ...

This article extends and builds upon our previous research concerning Virtual Reality (VR) with bilateral stimulation as an automated stress-reduction therapy tool. The study coincided with Russia's invasion of Ukraine, thus the software was ...

Computer mouse tracking is a simple and cost-efficient way to gather continuous behavioral data. As theory suggests a relationship between affect and sensorimotor processes, the computer mouse might be usable for affect sensing. However, the processes ...

Multi-modal classifiers for emotion recognition have become prominent, as the emotional states of subjects can be more comprehensively inferred from Electroencephalogram (EEG) signals and eye movements. However, existing classifiers experience a decrease ...

Emotion recognition systems are typically trained to classify a given psychophysiological state into emotion categories. Current platforms for emotion ground-truth collection show limitations for real-world scenarios of long-duration content (e.g. <inline-...

Emergent states are temporal group phenomena that arise from collective affective, behavioral, and cognitive processes shared among the group&#x0027;s members during their interactions. Cohesion is one such state, mainly conceptualized by scholars as ...

Automatic sensing of emotional information in speech is important for numerous everyday applications. Conventional Speech Emotion Recognition (SER) models rely on averaging or consensus of human annotations for training, but emotions and raters&#x2019; ...