Affect Intensity Estimation Using Multiple Modalities
Abstract
One of the challenges in affect recognition is accurate estimation of the emotion intensity level. This research proposes development of an affect intensity estimation model based on a weighted sum of classification confidence levels, displacement of feature points and speed of feature point motion. The parameters of the model were calculated from data captured using multiple modalities such as face, body posture, hand movement and speech. A preliminary study was conducted to compare the accuracy of the model with the annotated intensity levels. An emotion intensity scale ranging from 0 to 1 along the arousal dimension in the emotion space was used. Results indicated speech and hand modality significantly contributed in improving accuracy in emotion intensity estimation using the proposed model.
Cite
@article{arxiv.1607.01075,
title = {Affect Intensity Estimation Using Multiple Modalities},
author = {Amol Patwardhan and Gerald Knapp},
journal= {arXiv preprint arXiv:1607.01075},
year = {2016}
}
Comments
4 pages, 2 figures, 2 tables, Published as short paper in (Florida Artificial Intelligence Research Society Conference (2014)) Flairs 27 Conference. Peer reviewed and published paper, Key words: affect, intensity, multimodal, Kinect, emotion, face, body, hand, head, speech