About

One of the fast-growing fields of cognitive neuroscience is the study of emotions and their effects on cognitive processes. A very promising approach, which allows unique insights into human emotions, is the observation of humans’ facial expressions. Studies have shown that facial expressions are systematically associated with emotions such as anger, joy or fear. Therefore tracking facial movements permits the study of the underlying emotional experiences. Facial expressions tracking is traditionally a laborious process. However, advances in computer vision have rendered automatic facial expression detection fast and reliable. In this work we introduce FexMetrica, a toolbox developed to conduct statistical analyses of automatically detected facial expression time series. As with most time series data, there are nuisances due to the nature of the signal, which needs to be addressed before moving to statistical inference. Our toolbox is meant to provide a basic set of algorithms for these analyses, and the scaffold for the collaborative development of procedures for the study of facial expressions. Expected release date: 12 October 2015. In order to obtain access to FexMetrica before release date, email fexmetrica@gmail.com.