Improving Personalisation in Valence and Arousal Prediction using Data Augmentation

arXiv:2404.09042v1 Announce Type: new
Abstract: In the field of emotion recognition and Human-Machine Interaction (HMI), personalised approaches have exhibited their efficacy in capturing individual-specific characteristics and enhancing affective prediction accuracy. However, personalisation techniques often face the challenge of limited data for target individuals. This paper presents our work on an enhanced personalisation strategy, that leverages data augmentation to develop tailored models for continuous valence and arousal prediction. Our proposed approach, Distance Weighting Augmentation (DWA), employs a weighting-based augmentation method that …

abstract accuracy arxiv augmentation challenge cs.ai cs.cv cs.lg data emotion face however human human-machine interaction improving machine paper personalisation personalised prediction recognition type work