  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.
