The impact of weak ground truth and facial expressiveness on affect detection accuracy from time-continuous videos of facial expressions

In this paper we address two issues concerning real-world time-continuous emotion detection from videos of users’ faces: (i) the impact of weak ground truth on the emotion detection accuracy and (ii) the impact of the users’ facial expressiveness on the emotion detection accuracy. We implemented an appearance-based emotion detection algorithm that uses Gabor features and a k nearest neighbors classifier. We tested the performance of this algorithm on two datasets with different ground truth strengths (a firm ground truth dataset and a weak ground truth dataset). Then we split the dataset into three subsets reflecting different levels of users’ facial expressiveness (low, mid and high) and performed separate emotion detection.