Emotion pattern detection on facial videos using functional statistics
This work addresses the need for automated emotion recognition from facial videos, which is incremental as it builds on existing functional statistics methods for a specific application.
The paper tackled the problem of automatically analyzing facial expressions to recognize emotions from videos by proposing a technique based on Functional ANOVA to extract significant patterns of face muscle movements, and it used a functional F-test to determine time-related differences among emotional groups as a first step toward a reliable automatic emotion recognition system.
There is an increasing scientific interest in automatically analysing and understanding human behavior, with particular reference to the evolution of facial expressions and the recognition of the corresponding emotions. In this paper we propose a technique based on Functional ANOVA to extract significant patterns of face muscles movements, in order to identify the emotions expressed by actors in recorded videos. We determine if there are time-related differences on expressions among emotional groups by using a functional F-test. Such results are the first step towards the construction of a reliable automatic emotion recognition system