Detecting Cognitive Appraisals from Facial Expressions for Interest Recognition
This work addresses the problem of improving human-computer interaction through emotion recognition, but it is incremental as it builds on existing facial expression analysis methods.
The paper tackled the problem of automatically recognizing interest from facial expressions by studying relationships between facial action units and self-reported levels of interest and related cognitive appraisals, finding that direct detection is challenging due to variability in expressions.
Interest makes one hold her attention on the object of interest. Automatic recognition of interest has numerous applications in human-computer interaction. In this paper, we study the facial expressions associated with interest and its underlying and closely related components, namely, curiosity, coping potential, novelty and complexity. To this end, we conducted an experiment in which participants watched images and micro-videos while a front-facing camera recorded their expressions. After watching each item they self-reported their level of interest, curiosity, coping potential and perceived novelty and complexity. Using an automated method, we tracked facial action units (AU) and studied the relationship between the presence of facial movements with interest and its related components. We then tracked the facial landmarks, e.g., corners of lips, and extracted features from each response. We trained random forests regression models to detect the level of interest, curiosity, and appraisals. We found a large difference between the way people report and react to interesting visual content. The expressions in response to images and micro-videos were not always pronounced depending on the participants. This makes the direct detection of interest from facial expressions a challenging problem. With this work, for the first time, we demonstrate the feasibility of detecting cognitive appraisals from facial expressions which will open the door for appraisal-driven emotion recognition methods.