Subtle facial expression recognition using motion magnification

Abstract

This paper proposes a novel method for subtle facial expression recognition that uses motion magnification to transform subtle expressions into corresponding exaggerated ones. Motion magnification consists of four steps: First, active appearance model (AAM) fitting extracts 70 facial feature points in the face image sequence. Second, the face image sequence is aligned using the three feature points (two eyes and nose tip). Third, the motion vectors of 27 feature points are estimated using the feature point tracking method. Finally, exaggerated facial expressions are obtained by magnifying the motion vectors of the 27 feature points. After motion magnification, the exaggerated facial expressions are recognized as follows: first, the shape and appearance features are obtained by projecting the exaggerated facial expression image to the AAM shape and appearance model. Second, support vector machines (SVM) are used to classify shape and appearance features. Experimental results show that proposed subtle facial recognition rate is 88.125% for the 80 facial expression images in the SFED2007 database. (C) 2009 Elsevier B.V. All rights reserved.