Rotation invariant Facial Expression Recognition in image sequences

Facial Expression Recognition has mostly been done on frontal or near frontal faces. However, most of the faces in real life are non-frontal. This paper deals with in-plane rotation of faces in image sequences and considers the six universal facial expressions. The proposed approach does not need to rotate the image to frontal position. FER by rotating images to frontal is sensitive to determination of rotation angle and can involve errors in tracking facial points. Directions of motion of Facial Feature Points (FFPs) is used for feature extraction. In training for six expressions, Gaussian Mixture Models are fit to the distribution of angles representing these directions of motion. These models are used for further classification of test sequences using SVM. Gaussian Mixture Modeling is experimentally found to be robust to errors in position of FFPs. For dimensionality reduction, feature selection is performed using Fisher ratio test.