Pose invariant robust facial expression analysis
Khin Thu Zar Win, Fan Chen, Junko Izawa, Kazunori Kotani · 2010
This paper describes two novel facial expression recognition methods which are robust for head rotation within a certain angle range between -30 degrees and +30 degrees. We had proposed Eigenspace Method based on Class features of object (EMC) and Multiple Discriminant Analysis (MDA) for facial expression recognition. Our new methods, pEMC (parametric Eigenspace Method based on Class Features) and pMDA (parametric Multiple Discriminant Analysis), are extensions of EMC and MDA by using the parametric eigenspace technique. The parametric technique finds the manifold vector for recognition of rotated objects. Since EMC and MDA have the higher class separation, our new methods have both characteristics of parametric eigenspace and high classification of facial expression. pEMC and pMDA provide more 20 degree correct recognition than EMC regardless of the head pose.