In this paper, a face emotion is considered as the result of the composition of multiple concurrent signals, each corresponding to the movements of a specific facial muscle. These concurrent signals are represented by means of a set of multi-scale appearance features that might be correlated with one or more concurrent signals. The extraction of these appearance features from a sequence of face images yields to a set of time series. This paper proposes to use the dynamics regulating each appearance feature time series to recognize among different face emotions. To this purpose, an ensemble of Hankel matrices corresponding to the extracted time series is used for emotion classification within a framework that combines nearest neighbor and a majority vote schema. Experimental results on a public available dataset show that the adopted representation is promising and yields state-of-the-art accuracy in emotion classification.
|Numero di pagine||12|
|Stato di pubblicazione||Published - 2015|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)