Transactions on Affective Computing

In this paper, we propose a novel Electroencephalograph (EEG) emotion recognition method inspired by neuroscience with respect to the brain response to different emotions. The proposed method, denoted by R2G-STNN, consists of spatial and temporal neura…

Automatic prediction of emotion promises to revolutionise human-computer interaction. Recent trends involve fusion of multiple data modalities $-$- audio, visual, and physiological $-$- to classify emotional state. However, in practice, collection of p…

Temporal dominance (TD) methods can be used to record temporal changes in multiple sensory and affective responses. TD methods have found wide applications in the analysis of eating experiences of humans. However, extant analyses performed on TD data d…

Datasets play an important role in the progress of facial expression recognition algorithms, but they may suffer from obvious biases caused by different cultures and collection conditions. To look deeper into this bias, we first conduct comprehensive e…

This index covers all technical items – papers, correspondence, reviews, etc. – that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be …

The benefits of user-centered and participatory design have been widely acknowledged for the development of technologies that are likely to be appropriated by the product’s stakeholders. While participatory design has been applied to some affect…

Public speaking is a critical skill in daily communication. While more practicing such as rehearsal is helpful to improve such a skill, lack of personalized feedback limits the effectiveness of practicing. Therefore, we formulate the task of personaliz…

Emotion recognition is indispensable in human-machine interaction systems. It comprises locating facial regions of interest in images and classifying them into one of seven classes: angry, disgust, fear, happy, neutral, sad, and surprise. Despite sever…

The Biometric Attachment Test (BAT) is a recently developed psychometric assessment that exposes adults to standardized picture and music stimuli-sets while simultaneously capturing their linguistic, behavioral and physiological responses, with the goal of objectively measuring their psychological attachment characteristics. Within this work, (I) we describe a new version of the BAT (v2) that implements a remote photoplethysmography method to obtain physiological measures from video alone. (II) We discuss the specific challenges we found when trying to develop an automatic scoring algorithm for the BAT v2 using machine learning: practicing multimodal fusion over a high-dimensional feature space with a small learning sample. We propose and evaluate an original combination of methods, including a three-step hybrid multimodal fusion procedure, that overcomes these challenges. (III) Using the proposed methodology, we train a scoring algorithm for the BAT v2 on a francophone sample, using the Adult Attachment Questionnaire as ground-truth. (IV) We then validate the scoring algorithm cross-culturally, testing its performance on an independent anglophone sample, showing low error and high correlation and serving as the BAT v2’s first convergent validity evidence. We believe this work constitutes a breakthrough in the development of the first objective and automatic measure for adult attachment, and we hope that our “small data” learning methodology could be useful for other machine learning projects involving small samples coming from psychological research.

This study presents an efficient sparse learning-based pattern recognition framework to recognize the discrete states of three emotions—happy, angry, and neutral emotion—using electroencephalogram (EEG) signals. In affective computing wit…