Spontaneous vs. Posed smiles - can we tell the difference?

TL;DR

This paper investigates automatic classification of spontaneous versus posed smiles by analyzing facial motion and micro-expressions using SVM, achieving promising results on a large database.

Contribution

It introduces a method combining macro and micro facial motion analysis with SVM to distinguish spontaneous from posed smiles, improving classification accuracy.

Findings

Effective differentiation between spontaneous and posed smiles.

Utilization of facial fiducial markers and dense optical flow.

Promising classification accuracy on a large database.

Abstract

Smile is an irrefutable expression that shows the physical state of the mind in both true and deceptive ways. Generally, it shows happy state of the mind, however, `smiles' can be deceptive, for example people can give a smile when they feel happy and sometimes they might also give a smile (in a different way) when they feel pity for others. This work aims to distinguish spontaneous (felt) smile expressions from posed (deliberate) smiles by extracting and analyzing both global (macro) motion of the face and subtle (micro) changes in the facial expression features through both tracking a series of facial fiducial markers as well as using dense optical flow. Specifically the eyes and lips features are captured and used for analysis. It aims to automatically classify all smiles into either `spontaneous' or `posed' categories, by using support vector machines (SVM). Experimental results on large database show promising results as compared to other relevant methods.