Eulerian emotion magnification for subtle expression recognition

Abstract

Subtle emotions are expressed through tiny and brief movements of facial muscles, called micro-expressions; thus, recognition of these hidden expressions is as challenging as inspection of microscopic worlds without microscopes. In this paper, we show that through motion magnification, subtle expressions can be realistically exaggerated and become more easily recognisable. We magnify motions of facial expressions in the Eulerian perspective by manipulating their amplitudes or phases. To evaluate effects of exaggerating facial expressions, we use a common framework (LBP-TOP features and SVM classifiers) to perform 5-class subtle emotion recognition on the CASME II corpus, a spontaneous subtle emotion database. According to experimental results, significant improvements in recognition rates of magnified micro-expressions over normal ones are confirmed and measured. Furthermore, we estimate upper bounds of effective magnification factors and empirically corroborate these theoretical calculations with experimental data.

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