2017/01/18 by Daniel Hadar, Hadar, Daniel · 1 citation
Psychology · #Emotion and Mood Recognition #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Mental Health Research Topics
paper · pdf · doi:10.48550/arxiv.1701.05248
openalex publication_date 2017/01/18 · openalex created_date 2017/02/03 · openalex updated_date 2026/07/28
We present a method that automatically evaluates emotional response from spontaneous facial activity recorded by a depth camera. The automatic evaluation of emotional response, or affect, is a fascinating challenge with many applications, including human-computer interaction, media tagging and human affect prediction. Our approach in addressing this problem is based on the inferred activity of facial muscles over time, as captured by a depth camera recording an individual's facial activity. Our contribution is two-fold: First, we constructed a database of publicly available short video clips, which elicit a strong emotional response in a consistent manner across different individuals. Each video was tagged by its characteristic emotional response along 4 scales: Valence, Arousal, Likability and Rewatch (the desire to watch again). The second contribution is a two-step prediction method, based on learning, which was trained and tested using this database of tagged video clips. Our method was able to successfully predict the aforementioned 4 dimensional representation of affect, as well as to identify the period of strongest emotional response in the viewing recordings, in a method that is blind to the video clip being watch, revealing a significantly high agreement between the recordings of independent viewers.