Published on Tue Jul 14 2015

Ensemble of Hankel Matrices for Face Emotion Recognition

Liliana Lo Presti, Marco La Cascia

A face emotion is considered as the result of the composition of multiple concurrent signals. The extraction of these appearance features from asequence of face images yields to a set of time series. An ensemble of Hankel matrices is used for emotion classification.

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Abstract

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 shows that the adopted representation is promising and yields state-of-the-art accuracy in emotion classification.